What is Bias in neural networks?
In my Artificial Intelligence class the teacher addressed the subject about neural networks, which in the case, neural networks have the layers, such as: input, hides and output and the neurons that make them up.
However, he cited the term bias which seems to me to be a neuron, however, this term made me more confused regarding neural networks and I would like to have this doubt clarify.
Doubt
What would Bias be about neural networks?
4 answers
Simply put, Bias is a value input "1" associated with a weight "b" in each neuron. Its function is to increase or decrease the liquid input, so as to transfer the activation function on the axis.
Example:
To approximate a set of points to a line, we use y = a*x + b*1, where a and b are constants. x eh an input associated with a weight a and we have a weight b associated with input 1.
Now imagine that the network activation function is a function linear.
In the neural network, some inputs are supplied to an artificial neuron, and with each input, a weight is associated. The weight increases the inclination of the activation function. This means that the weight decides how quickly the activation function will be activated, while polarization is used to delay the activation function's activation.
For a typical neuron, if the inputs are x1, x2 and x3, the synaptic weights to be applied to them will be denoted as w1, w2 e w3.
Weight shows the effectiveness of a particular input. The higher the input weight, the more it will influence the neural network.
On the other hand, Bias is like The Intercept added in a linear equation. It is an additional parameter in the Neural network that is used to adjust the output next to the weighted sum of the inputs to the neuron. That is, Bias is a constant that helps the model in a way that it can better adapt to the data provided.
If there is no "bias", the model will train on the point passing only by the origin, which is not in accordance with the"real world". Also with the introduction of bias, the model will become more flexible.
Finally bias helps control the value at which the activation function will be activated.
The mathematical neuron model may also include an input bias or bias. This variable is included in the summation of the activation function, in order to increase the degree of freedom of this function and, consequently, the ability to approach the network. The bias value is adjusted in the same way as the synaptic weights. Bias allows a neuron to present non-null output even though all its inputs are null. For example, if there was no bias and all inputs of a neuron were null, so the value of the activation function would be null. In this way we could not, for example, make the neuron learn the relation pertinent to the "or exclusive" of logic.
Source: http://deeplearningbook.com.br/o-neuronio-biologico-e-matematico /
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Imagine the following: Every day you take go to the bakery, buy some things to eat and when it comes home you take coffee. But sometimes you buy bread, sometimes you buy cake or other things, but you always buy coffee to drink. the bias is that, the coffee, is the constant value that regardless of the other values this value will always have there. That is, if your coffee costs 3.50 every time, sometimes the other things you buy can cost 10 reais, 7 reais, these are the values of the entrance, but you will always have your bias costing 3,50 that is your coffee.