The document discusses the perceptron algorithm, which is a simple neural network used for binary classification. It was invented in 1957 and works by computing weighted inputs and applying a threshold activation function. The perceptron learns by adjusting its weights during the training process. It is computationally efficient but can only learn linearly separable problems and not more complex nonlinear relationships.

1. Perceptron Algorithm
Md. Zul Kawsar
130302005

2. Content
 Introduction
 Application
 Working Procedure
 Strength & Weakness
 Conclusion
 References
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3. Introduction
 Perceptron is a simplest form of a neural network used for the classification of
patterns.
 The perceptron algorithm was invented in 1957 at the Cornell Aeronautical
Laboratory by Frank Rosenblatt.
 Frank Rosenblatt was a Psychologis.
Mark I Perceptron machine
The machine was connected to a camera that used
20×20 cadmium sulfide photocells to produce a 400-
pixel image
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4. Introduction (Cont..)
 We Consider Linearly Separable Pattern as Example:
• Here S1 and S2 are two type of records .
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5. Application
 In machine learning, the perceptron is an algorithm for supervised
classification of an input into one of several possible non-binary outputs.
 Perceptron can be defined as a single artificial neuron that computes its
weighted input with the help of the threshold activation function or step
function.
 The Perceptron is used for binary Classification.
 The Perceptron can only model linearly separable classes.
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6. How it works
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7. How it works (cont..)
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8. How it works (cont..)
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9. Strength and Weakness
 Strength
• Simple and computationally efficient.
• Guaranteed to learn a linearly separable problem.
 Weakness
• Only linear separations.
• Only converges for linearly separable data.
• Not really efficient with many features.
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10. Conclusion
 Simple classification rule base on weight vector.
 Simple online learning algorithm guaranteed to converge if training set is
separable.
 With more than two class, perceptron uses one neuron to model a class against
the others.
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11. References
 https://en.wikipedia.org/wiki/Perceptron
 Pattern Classification
Richard O.Dude
Peter E.Hart
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12. Thank You
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