Hands-On Mathematics for Deep Learning

Pros and cons of the MP neuron and perceptron

The advantage the perceptron model has over the MP neuron is that it is able to learn through error correction and it linearly separates the problem using a hyperplane, so anything that falls below the hyperplane is 0 and anything above it is 1. This error correction allows the perceptron to adjust the weights and move the position of the hyperplane so that it can properly classify the data.

Earlier, we mentioned that the perceptron learns to linearly classify a problem—but what exactly does it learn? Does it learn the nature of the question that is asked? No. It learns the effect of the input on the output. So, the greater the weight associated with a certain input, the greater its impact on ...