Summary
Human brains are connected like a neural net, with neurons forming links called synapses.
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These synapses are capable of strengthening or weakening depending on how active they are, a process known as plasticity.
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Artificial neural networks are modeled loosely on the human brain, with thousands or even millions of interconnected nodes.
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They are feed-forward, meaning data moves through them in one direction
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, and are used to train models such as the XOR function.
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Artificial neurons use continuous values with continuous activation functions, and are trained using the backpropagation algorithm.
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According to
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Summary
Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, and they’re “feed-forward,” meaning that data moves through them in only one direction.
Explained: Neural networks | MIT News | Massachusetts Institute of ...
mit.edu
Learn about neural networks that allow programs to recognize patterns and solve common ... the human brain could produce complex patterns through connected ...
What are Neural Networks? | IBM
ibm.com
Summary
Artificial neurons and perceptrons were inspired by the biological processes scientists were able to observe in the brain in the 1950s, but they differ from their biological counterparts in several ways. Artificial neurons use continuous values with continuous activation functions, which are used to train multiple layers of neural networks, and they are able to learn more complex relationships in data by using the backpropagation algorithm. Artificial neurons are now being used to train models such as the XOR function, which is used to learn a simple XOR function, and are being used to train models such as the XOR function.
The differences between Artificial and Biological Neural Networks | by Richard Nagyfi | Towards Data Science
towardsdatascience.com