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What makes humans so unique is that we can learn as we go, drawing on our memories and experiences. That means taking in data from the world around us and forming ideas about how to optimally perform tasks or understand new information.
Deep learning, which is a branch of artificial intelligence, aims to replicate our ability to learn and evolve in machines. At the end of the day, deep learning allows computers to take in new information, decipher it, and produce an output—all without humans needing to be involved in the process. This field has enormous implications for the technologies of the future, including self-driving vehicles, facial recognition software, personalized medicine, and much more...
Copying the Brain’s Connections
Neural networks are so-named because they essentially aim to mimic the functioning of neurons in the human brain. These networks are made up of three layers of digital neurons: the input layer, the hidden layer, and the output layer.
The input layer is a series of digital neurons that “see” the information the computer is being given. One neuron might fire when the color green is present in an image, for example, while another might fire when a particular shape is present. There can be thousands of input layer neurons, each firing when it sees a specific characteristic in the data.
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Source: Techradar