From e1d6e268d9d9b8695f9da41026a21ecaec0cb018 Mon Sep 17 00:00:00 2001 From: Vernika Gupta Date: Fri, 5 Jul 2019 13:58:35 +0530 Subject: [PATCH] difference between perceptron and sigmoid --- 1- Neural Networks and Deep Learning/Readme.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/1- Neural Networks and Deep Learning/Readme.md b/1- Neural Networks and Deep Learning/Readme.md index 39c85c7c..f1a79282 100644 --- a/1- Neural Networks and Deep Learning/Readme.md +++ b/1- Neural Networks and Deep Learning/Readme.md @@ -70,9 +70,14 @@ Here are the course summary as its given on the course [link](https://www.course ### What is a (Neural Network) NN? - Single neuron == linear regression without applying activation(perceptron) -- Basically a single neuron will calculate weighted sum of input(W.T*X) and then we can set a threshold to classify in a perceptron. +- Basically a single neuron will calculate weighted sum of input(W.T*X) and then we can set a threshold to predict output in a perceptron. If weighted sum of input cross the threshold, perceptron fires and if not then perceptron doesn't predict. - Perceptron can take real values input or boolean values. +- Actually, when w⋅x+b=0 the perceptron outputs 0. +- Disadvantage of perceptron is that it only output binary values and if we try to give small change in weight and bais then perceptron can flip the output. We need some system which can modify the output slightly according to small change in weight and bias. Here comes sigmoid function in picture. +- If we change perceptron with a sigmoid function, then we can make slight change in output. +- e.g. output in perceptron = 0, you slightly changed weight and bias, output becomes = 1 but actual output is 0.7. In case of sigmoid, output1 = 0, slight change in weight and bias, output = 0.7. - If we apply sigmoid activation function then Single neuron will act as Logistic Regression. +- we can understand difference between perceptron and sigmoid function by looking at sigmoid function graph. - Simple NN graph: - ![](Images/Others/01.jpg)