In the perceptron model, the weight ww vector is perpendicular to the linear decision boundary at all times. True False

Can a Perceptron be made to form non-linear decision boundary? (Choose the best answer)(1 點)

Assume a perceptron:with 3 inputs (x1,x2,x3) plus a bias (x0) statically set to 1with weighted input= x0*w0+x1*w1+x2*w2+x3*w3that outputs 1 if weighted input > 0, else 0with initial weights are all set to 0with weight updating as follows: Wi j+1= Wi j+ a * (Target j- Output j) * X i and a learning rate a=1 How will the final weight vector look like when all data-items are processed? 1 0 1 1 0 0 -1 0 0 -1 0 0 1 0 1 0 None of the above

Q.No 10. Which of the following is false for the statement“perceptron learning algorithm,”a) Can only learn linearly separable problemsb) Updates its weights based on the sign of the errorc) Requires a pre-defined learning rated) Guarantees finding an optimal solution for any dataset

hat is the primary purpose of the weight matrix in a perceptron during the training process? a. To store input data b. To define the threshold value c. To calculate the error gradient d. To adjust the connection strengths between neurons

Assume a 3-input perceptron plus bias (it outputs 1 if net > 0, else 0). Assume a learning rate c of 1 andinitial weights 0. The perceptron learning rule is: ∆wi = c(t – z) x i . Given the following training data set:1 0 0̶ ̶ > 00 1 1̶ ̶ > 11 0 1̶ ̶ > 11 1 0̶ ̶ > 01 1 1̶ ̶ > 00 0 1̶ ̶ > 0Please demonstrate the learning process for 1 epoch by filling the following table:Pattern Target Weight Vector Net Output ∆W1 0 0 1 00 1 1 1 1