In a Neural network, weight increases the steepness of activation function and it decides how fast the activation function will trigger whereas bias is used to delay the triggering of the activation function.

For a typical neuron model, if the inputs are a1,a2,a3, then the weight applied to them are denoted as h1,h2,h3. Then the output is as follows:

y = f(x) = Σa*i*h*i*

Where i is the number of inputs.

This shows us the effectiveness of a particular input. The weight of input is directly proportional to the impact of the network.

Bias is like the intercept added in a linear equation. It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. Thus, Bias is a constant which helps the model in a way that it can fit best for the given data.

The processing done by the neuron is:

output = sum (weights * inputs) + bias

For example, consider an equation y=mx+c

Here m is acting as weight and the constant c is acting as bias.

Due to the absence of bias, the model will train over point passing through origin only, which is not in accordance with a real-world scenario. Also with the introduction of bias, the model will become more flexible.

It helps the model to learn the patterns and it acts like an input node that always produces constant value 1 or other constant. Because of this property, they are not connected to the previous layer.

Bias units are not connected to any previous layer, it is just appended to the start/end of the input and each hidden layer, and is not affected by the values in the previous layer.

You can refer to this diagram:

Here x1,x2,x3 are inputs and w1, w2,w3 are weights. It takes an input, processes it, passes it through an activation function, and returns the output.

Watch this video to learn about Neural Networks: