After revising the concepts around learning from data, we will now pay close attention to an algorithm that trains one of the most fundamental neural-based models: the perceptron. We will look at the steps required for the algorithm to function, and the stopping conditions. This chapter will present the perceptron model as the first model that represents a neuron, which aims to learn from data in a simple manner. The perceptron model is key to understanding basic and advanced neural models that learn from data. In this chapter, we will also cover the problems and considerations associated with non-linearly separable data.
Upon completion of the chapter, you should feel comfortable discussing the perceptron model, and applying its learning algorithm. You will be able to implement the algorithm over both linearly and non-linearly separable data.
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