What is Deep Learning?

Deep learning is a sort of machine learning (ML) and artificial intelligence (AI) that reflects the way humans obtain certain types of information. Deep learning is an imperative element of data science, which includes statistics and forbidding modeling. It is greatly beneficial to data scientists who are tasked with collecting, analyzing, and interpreting large volumes of data; deep learning brings this process quick and simple.

At its simplest, deep learning can be conceived of as a way to automate guess analytics. 

To learn about deep learning, consider a toddler whose 1st word is a dog. The toddler learns and understands what a dog is -- and what is not -- by pointing to objects and uttering the word dog. The parents say, "Yes, It is a dog," or, "No, It is not a dog." As the toddler points again to objects, he made more aware of the features that all dogs exhibit. What the toddler will do, without knowing it, is to clarify a complex concept-- the concept of dog behavior-- by building a hierarchy in which each level of abstraction is created with the knowledge that was gained from the previous layer of the chain hierarchy.

Deep-learning environment

Computer programs that use deep learning concepts go through much the same method as the toddler learning to recognize the dog. Each algorithm in the hierarchy applies a nonlinear transformation in its input and utilizes what it learns for creating a statistical model as output. 

In traditional machine learning concepts, the learning process is evaluated, and the programmer has to be very particular when executing the computer what types of things it should be looking to decide if a picture contains a dog or doesn't contain a dog. This is a tiresome process called feature extraction, and the computer's success rate depends entirely upon the coder's ability to precisely define a feature set for "dog." The benefit of deep learning is the program builds the feature set by itself without supervision. 

Initially, the computer program might be provided with 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 and a tail should be labeled "dog." Of course, the program is not aware of the labels "four legs" or "tail." It will simply look for patterns of pixels in the digital data. With each iteration, the predictive model becomes more complex and more accurate.

Unlike the toddler, who will take 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.

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