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Perceptron: The Basic Artificial Neuron

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History and Concept: Frank Rosenblatt invented Perceptron program in 1957 on IBM 704 computer
Perceptron simulates brain principles with learning and decision-making capabilities
Original Perceptron processes binary inputs to produce one binary output
Key Components: Input nodes have binary values (1 or 0) and corresponding weights
Weights represent input importance, higher values indicate stronger influence
Perceptron calculates weighted sum and applies activation function
Threshold value determines whether neuron fires (output 1) or remains inactive (output 0)
Learning and Limitations: Perceptron learns through training by adjusting weights based on errors
Cannot make decisions alone, requires multiple neurons for complex tasks
Limited to linearly separable patterns, can be extended with multi-layer perceptrons
Forms foundation for more complex neural networks like deep neural networks