Week 3 of natural computation continued our step by step unraveling of the perceptron. We dealt with the case of classification and supervised training of a single perceptron. Although the concepts and logic are quite straight forward, there was some odd math operations that we spent a lot of our time on. I am not sure why we spent so much time on drawing the discriminant of a perceptron. I can’t see how it could be a useful skill with the exception of when we need to hand draw boundaries in the exam (and learning something just to pass an exam seems nonsensical). Anyway, the tutorial was particularly good in that we did some practical calculations in excel that were closely correlated to what we learnt in the lecture.
Specifically, the process of training a perceptron was emulated. Seeing exactly how altering the Beta value changed the learning process for a perceptron was valuable, along with understanding some of the possible inefficiencies/intractabilities associated with the simple, single perception network.
I am really looking forward to when we can see how an MLP handles a large dataset, sometimes having a vision of the goal makes these simple steps much more understandable.