This notebook was born while I was serving as TA on MIT's Data Science MicroMasters course https://micromasters.mit.edu/ds/. I felt that after grasping the mathematical process of the linear perceptron, there was still a need to build an intuitive understanding of how the updates in the algorithm, taken together, lead to the final classifier.
This notebook aims to do that by presenting various graphical representations, not just of the final classifier, but of the process involved in getting there.
After that intuition has been built, the notebook expands on this by asking original questions about the process which I haven't found elsewhere. These questions challenge the student to think more deeply beyond what they have been told, and to develop a deeper understanding beyond simple regurgitation of what they heard.
I hope that it is of help!
Gabriel