Deep learning is a subset of Artificial Intelligence, which is an area that relies on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, these models work with artificial neural networks, designed to mimic the way humans think and learn.
Deep learning – complex neural networks – is designed to mimic the workings of the human brain so that computers can be trained to deal with ill-defined abstractions and problems.
Where Deep Learning is used?
An average five-year-old kid can easily find the difference between the face of his teacher and that of the crossing guard. In contrast, the computer has to do a lot of work to understand Who’s Who. Neural networks and deep learning are often used similarly in the tasks of object recognition, speech recognition, and various other computer vision tasks.
How Deep Learning Works?
To understand the process followed by a deep learning model, we first need to know what is a neural network, because a deep learning model is all about neural networks. So what are Neural Networks? A neural network is also a kind of machine learning model which is inspired by the neurons in the human brain. It mainly consists of three or more layers. An input layer, one hidden layer or even more hidden layers, and an output layer.
While training these deep neural networks model, the Data is injected through the input layer. Then the data is changed in the hidden layer and the output layers based on the weights applied to those nodes. Generally, a Neural network is surrounded by thousands of millions of nodes which are densely interconnected with each other.
The term deep learning is used when there are multiple hidden layers in a neural network. Using an iterative approach, a neural network continually adjusts and makes inferences until a specific breakpoint is reached.
Typically, these models learn from unlabeled and unstructured data. While deep learning is very similar to a traditional neural network, it will have many more hidden layers. The more complex the problem, the more hidden layers there will be in the model.
There are many areas in which these models are used, such as the voice recognition feature helps companies keep their customers secure by providing voice recognition features in their smartphones to their cars. Deep learning algorithms can even help the Investigation departments to track the movements of a known suspect.
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