What is Perceptron in Deep Learning?

To get the better understanding of neural networks, we first need to understand what is perceptron? Perceptron is nothing but its a type of artificial neuron. Artificial neuron is nothing but it mimics our brain as explained in my previous post.

Let's understand what is perceptron and how does perceptron algorithm works. As per the below image we are adding some inputs and every input as specific weight attached to it. The weights define how important the input is. After multiplication all are summed together and send it to activation function. The reason for using activation function is to use the threshold value. If the signal is above that threshold value the neuron or perceptron will fire else it will not. There are different kind of activation functions like sigmoid, step function, sign function depending on use case. The main idea was to define an algorithm in order to learn the values of weights which are then multiplies with the input features in order to make a decision whether the neuron will fire or not. We can even call perceptron as single layer binary linear classifier because it is able to classify the inputs which are linearly separable.

Steps of Perceptron Learning Algorithm
1. Initialize the weights and threshold
2. Provide the input and calculate output
3. While training artificial neuron we have set of output values. So we know the output values. To test it, we give the input and check it’s output and match it with the existing output.
4. If the output doesn’t match with the given one, in that case we need to update the weights accordingly in order to reduce the loss.

People who read this post also read :