What is perceptron? Explain.
Himanshu KulshreshthaElite Author
Asked: April 6, 20242024-04-06T13:24:06+05:30 2024-04-06T13:24:06+05:30In: Development and Management
What is perceptron? Explain.
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A perceptron is a fundamental building block of artificial neural networks, specifically a type of single-layer neural network used for binary classification tasks. It was introduced by Frank Rosenblatt in the late 1950s and is based on the concept of a simplified model of a biological neuron.
A perceptron consists of:
Mathematically, the output (y) of a perceptron can be expressed as:
[ y = \text{activation_function}(z) = \text{activation_function}(w_1x_1 + w_2x_2 + \ldots + w_nx_n + b) ]
The perceptron learning process involves:
The perceptron learning rule updates the weights and bias as follows:
[ w_i \leftarrow wi + \alpha \times (y{\text{true}} – y_{\text{pred}}) \times xi ]
[ b \leftarrow b + \alpha \times (y{\text{true}} – y_{\text{pred}}) ]
Where:
Perceptrons are limited to linear decision boundaries and are only capable of learning linearly separable patterns. However, they laid the groundwork for more complex neural network architectures, such as multi-layer perceptrons (MLPs), which can learn non-linear patterns and are used extensively in modern machine learning applications for tasks like image recognition, natural language processing, and more complex classifications.