# Weights and Biases

Weights and biases (commonly referred to as *w and b*) are the learnable parameters of a some machine learning models, including neural networks.

Neurons are the basic units of a neural network. In an ANN, each neuron in a layer is connected to some or all of the neurons in the next layer. When the inputs are transmitted between neurons, the weights are applied to the inputs along with the bias.

![A neuron](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-LvNWUoWieQqaGmU_gl9%2F-LvNczsh9MyG8YDoWJi-%2Fimage.png?alt=media\&token=3b38a6c3-f8cb-4d2b-b388-6b1e237cbdf3)

**Weights** control the signal (or the strength of the connection) between two neurons. In other words, a weight decides how much influence the input will have on the output.

**Biases**, which are constant, are an additional input into the next layer that will always have the value of 1. Bias units are not influenced by the previous layer (they do not have any incoming connections) but they do have outgoing connections with their own weights. The bias unit guarantees that even when all the inputs are zeros there will still be an activation in the neuron.

![Souce: Mate Labs](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-LvI8vNq_N7u3RWVAPLk%2F-LvJSdcFXzoI-WW0L3w5%2Fimage.png?alt=media\&token=84526dc6-4634-4de5-aacf-00a179afac76)


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