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In my Artificial Intelligence class the teacher addressed the subject of neural networks, which in this case, neural networks have the layers, such as: entree, occult and exit and the neurons that make them up.
However, he quoted the term bias which seems to me to be a neuron, but this term has left me more confused in relation to neural networks and I would like to have this question clarified.
What would Bias be about neural networks?
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Simply put, Bias is an input of value "1" associated with a weight "b" in each neuron. Its function is to increase or decrease the net input in order to translate the activation function on the axis.
Example:
To approximate a set of points to a straight line, we use y = a*x + b*1, where a and b are constant. x is an entry associated with a weight a and we have a weight b associated with entry 1.
Now imagine that the network activation function is a linear function.
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In the neural network, some inputs are provided to an artificial neuron and, at 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 the polarization is used to delay the activation function.
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 and w3.
The weight shows the effectiveness of a specific input. The higher the input weight, the more it will influence the neural network.
On the other hand, Bias is like the intercept added into a linear equation. It is an additional parameter in the Neural Network that is used to adjust the output together with 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 to control the value at which the activation function will be activated.
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The mathematical neuron model may also include a polarization or input bias. This variable is included in the sum of the activation function, in order to increase the degree of freedom of this function and, consequently, the ability to approximate the network. The bias value is adjusted in the same way as the synaptic weights. Bias makes it possible for a neuron to show non-zero output even though all of its inputs are null. For example, if there were no bias and all inputs of a neuron were null, then the value of the activation function would be null. In this way we could not, for example, cause the neuron to learn the relation pertinent to the "or exclusive" of logic.
Source: http://deeplearningbook.com.br/o-neuronio-biologico-e-matematico/
Read this book he’s excellent!
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Imagine the following: Every day you go to the bakery, buy some things to eat and when you come home you have coffee. However sometimes you buy bread, sometimes buy cake or other things, but you always buy coffee to take. 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 always, sometimes the other things you buy can cost 10 real, 7 real, those are the values of the entrance, but you will always have your bias costing 3.50 which is your coffee.
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It’s the plural of Bradesco’s assistant :P
– Maniero