1. Perceptron Models The perceptron is a kind of binary classifier that maps its input x (a vector of type Real) to an output value f(x) (a scalar of type Real) calculated asvectorRealscalar f(x) = + b where w is a vector of real-valued weights and is the dot product(which computes a weighted sum). b is the 'bias', a constant term that does not depend on any input value.(x)dot product

3. x(j) denotes the j-th item in the input vector w(j) denotes the j-th item in the weight vector y denotes the output from the neuron δ denotes the expected output α is a constant and 0 < α < 1 the appropriate weights are applied to the inputs that passed to a function which produces the output y The weights are updated after each input according to the update rule below: w(j)' = w(j) + α(δ − y)x(j)

4. Famous Minsky and Papert Book:Perceptrons (1969) Showed that Perceptrons couldn’t solve general simple nonseperable problems (eg. XOR)