What is a perceptron? A perceptron is a single layer neural network. It is a binary classifier (i.e, linear classifier) which helps to classify the given input data.
- Perceptron can represent basic logic gates such as OR, AND, NOT gates as well as NAND, NOR gates.
- Perceptron can be usefull in classifying any data that is linearly separable. It may not be well suitable for non-linear classification.
A perceptron consists of 4 parts:
- Input values
- Weights and bias
- Summation
- Activation Function
Perceptron Training Rule
- Initialize with random weights(w).
- For each example(x) in training data D, perform the following steps: a. Calculate the actual output y(t) = w0.x0 + w1.x1 + ... + wn-1.xn-1 b. Update the weights w = w + (y(t) - O) where, y(t) = actual output O = class output
Activation Function: Various activation functions that can be used are:
- Unit step.
- Linear.
- Sigmoid or Logistic.
- Hyperbolic tangent.
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