Perceptron vs Logistic Regression


What are differences between logistic regression and a Perceptron. I know perceptrons are used for building NNs but looking just at the single perceptron identity I’m not able to figure out the difference.

Similar properties:

  1. Both perform weighted sum on inputs to reach an intermediate output.
  2. Both perform transformation on this intermediate output to reach final output. In logistic regression, we use sigmoid function for this purpose.
  3. In both architectures, we update the weights based on error. We apply gradient descent in both the architectures.
  4. Both are fundamentally used to classify data points which are linearly separable.

Can i say perceptron is logistic regression with some cosmetic changes?

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