A bias node, often simply called a “bias”, is a term used in artificial intelligence, especially in the field of neural networks.
When training a neural network to learn from data, we need it to be flexible enough to fit different patterns and structures in the data. This is where the bias node comes in.
You can think of a bias node like the intercept term in a linear equation (y = mx + b). In that equation, ‘b’ is the bias. It allows the line to move up and down to better fit the data, instead of always going through the origin (0,0).
Similarly, in a neural network, a bias node allows the output of the network to shift along the output axis to better fit the data. Without a bias node, the network would always have to pass through the origin, which could limit its ability to learn and fit the data properly.
So, a bias node adds an extra degree of freedom to the network, enhancing its learning capability. The value of the bias node, like other parameters in the network, is learned during the training process.
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