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Perceptron Model
This article covers the content discussed in the Perceptron module of the Deep Learning course and all the images are taken from the same module.
In this article, we discuss the 6 jars of the Machine Learning with respect to the Perceptron model.
Our job in Machine Learning and in general in Deep Learning also is to find a function that captures the relationship between the input and the output. And this function has parameters(it could be the weights for the inputs, bias terms or some other parameters). So, our job is to come up with the function and its parameters using the data that we have.
Perceptron model tries to overcome the limitations of the MP Neuron model which are depicted below in terms of 6 jars of ML.
Perceptron model would overcome some of the limitations of the MP Neuron which we discuss in this article:
Perceptron Data Task:
When dealing with MP Neuron, the data that we could feed to the neuron was all the Boolean data and that lead to some unnatural decisions because, for example, in the real world we would like to have the absolute value of the weight instead of saying like it’s heavy…