Introduction
We all know that an Artificial Neuron is a basic building block of the neural network. Before we get into the topic, “what is the role of weights and bias in a Neural Network “, let us understand the skeleton of this Artificial Neuron.
image by the Author
Components of the basic Artificial Neuron:
- Inputs: Inputs are the set of values for which we need to predict the output value. They can be viewed as features or attributes in a dataset.
- Weights: weights are the real values that are associated with each feature which tells the importance of that feature in predicting the final value. (we will know more about in this article)
- Bias: Bias is used for shifting the activation function towards left or right, it can be referred to as a y-intercept in the line equation. (we will know more about this in this article)
- Summation Function: The work of the summation function is to bind the weights and inputs together and find their sum.
- Activation Function: It is used to introduce non-linearity in the model.
What if there are no weights?
We will come to know one’s importance only during its absence
As the statement speaks, let us see what if there are no weights involved in a neuron, for simplicity let us consider there are only two features in the dataset, ie input vector** X ϵ [ x₁ x₂ ],** and our model task it to perform binary classification.
image by the Author
The summation function** g(x)** sums up all the inputs and adds bias to it.
and the role of the activation function is to allocate the data points to one of the classes.
If we compare the model expression with the equation of a line:
we can see that the slope(m) of the equation x₂ = -x₁ + b is fixed that is -1, and it will not change in any case for any dataset, that’s the problem of not having weights in our model, we are not able to draw a scalable line that separates two classes**.**
#data #neural-networks #artificial-intelligence #machine-learning #deep-learning
