learning is an aspect of artificial intelligence (AI) that is
concerned with emulating the learning approach that human beings use to gain
certain types of knowledge. At it's simplest, deep learning can be thought of
as a way to automate predictive analytics. Use cases for deep learning
include all types of big data analytics applications, especially those
focused on natural language processing (NLP), language translation, medical
diagnosis, stock market trading signals, network security and image
While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. To understand deep learning, imagine a toddler whose first word is "dog." The toddler learns what is (and what is not) a dog by pointing to objects and saying the word "dog." The parent might say "Yes, that is a dog" or "No, that is not a dog." As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is to clarify a complex abstraction (the concept of dog) by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.
Computer programs that use deep learning go through much the same process. Each algorithm in the hierarchy applies a non-linear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the word "deep."
In traditional machine learning, the programmer has to be very, very specific when telling the computer what types of things it should be looking for when deciding if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction and the computer's success rate depends entirely upon the programmer's ability to accurately define a feature set for dog. The advantage of deep learning is that the program builds the feature set by itself. This is not only faster, it is usually more accurate.
Initially, the computer program might be provided with a training data, a set of images for which a human has labeled each image "dog" or "not dog" with meta tags. The program uses the information it receives from the training data to create a feature set for dog and build a predictive model. In this case, the model the computer first creates might predict that anything in an image that has four legs should be labeled dog. Of course, the program is not aware of the label "four legs," it will simply look for patterns in the digital data. With each iteration, the predictive model the computer creates becomes more complex and more accurate.
Because this process mimics human thought, deep learning is sometimes referred to as deep neural learning or deep neural networking. Unlike the toddler, who will takes weeks or even months to understand the concept of "dog," a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have dogs in them within a few minutes.
In order to achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming is able to create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the Internet of Things (IoT) continues to become more pervasive, because most of the data humans and machines create is unstructured and is not labeled.
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