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Hello, Perceptron (2023)
Outline History makes for good pedagogy with neural networks. The simplest possible artificial neural network contains just one very simple artificial neuron – Frank Rosenblatt’s original perceptron.
So, operationally, a perceptron treats an input as a vector of features (each represented by a number) and computes a weighted sum, before applying a step function to determine the output. By adding hidden layers, MLP networks can model more complex, non-linear relationships between inputs and outputs, effectively overcoming the limitations of single perceptrons. With these changes, ANNs become capable of learning complex, non-linear functions and solving a wide range of problems, ultimately leading to the development of the powerful generative AI models we see today.
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