# Bias Neuron

(Redirected from Neural Network Bias)

A Bias Neuron is an Artificial Neural Network Node that is linked to every hidden nodes.

**AKA:**Bias Unit, Bias Node, Neural Network Bias.**Context:**- …

**Example(s):**- (Zhao,2016) ⇒ 3-layer Fully Connected Neural Network Bias Neurons:

- (Zhao,2016) ⇒ 3-layer Fully Connected Neural Network Bias Neurons:
**Counter-Example(s):****See:**Neural Network Weight, Neural Network Layer, Data Preprocessing Task, Neural Network Weight Initialization, Batch Normalization, Neural Network Regularization.

## References

### 2016b

- (Zhao, 2016) ⇒ Peng Zhao, February 13, 2016. "Weight and Bias"R for Deep Learning (I): Build Fully Connected Neural Network from Scratch
- QUOTE: Take above DNN architecture, for example, there are 3 groups of weights from the input layer to first hidden layer, first to second hidden layer and second hidden layer to output layer. Bias unit links to every hidden node and which affects the output scores, but without interacting with the actual data. In our R implementation, we represent weights and bias by the matrix. Weight size is defined by,(number of neurons layer M) X (number of neurons in layer M+1)
and weights are initialized by random number from rnorm. Bias is just a one dimension matrix with the same size of neurons and set to zero. Other initialization approaches, such as calibrating the variances with 1/sqrt(n) and sparse initialization, are introduced in weight initialization part of Stanford CS231n.

- QUOTE: Take above DNN architecture, for example, there are 3 groups of weights from the input layer to first hidden layer, first to second hidden layer and second hidden layer to output layer. Bias unit links to every hidden node and which affects the output scores, but without interacting with the actual data. In our R implementation, we represent weights and bias by the matrix. Weight size is defined by,