A perceptron has three inputs X₁, X₂, X3 and a bias input fixed to 1. It classifies using a thresholdstep function [h(z)= 1 if z>0; else 0] as its activation function. Initialize the corresponding weightsas follows: w₁ = 0.7, W₂ = 0.2, W3 = 0.9. Let the learning rate be 0.2 and bias weight w. be -0.7.W₁X1W₂X₂0W3WAX3+1Apply the perceptron learning rule with the above input and with desired output 0. What arethe new weights after this update ? Note: The output 0 here is fixed and may or may not bethe answer to the previous question.

A hidden layer in a neural network is literally just adding neurons between the input and output…

A perceptron has three inputs X₁, X₂, X3 and a bias input fixed to 1. It classifies using a threshold step function [h(z)= 1 if z>0; else 0] as its activation function. Initialize the corresponding weights as follows: w₁ = 0.7, W₂ = 0.2, W3 = 0.9. Let the learning rate be 0.2 and bias weight w. be -0.7. W₁ X₁ W₂ X₂ 0 W3 W₂₁ X3 +1 Apply the perceptron learning rule with the above input and with desired output 0. What are the new weights after this update ? Note: The output 0 here is fixed and may or may not be the answer to the previous question.

A perceptron has three inputs X₁, X₂, X3 and a bias input fixed to 1. It classifies using a threshold step function [h(z)= 1 if z>0; else 0] as its activation function. Initialize the corresponding weights as follows: w₁ = 0.7, W₂ = 0.2, W3 = 0.9. Let the learning rate be 0.2 and bias weight w. be -0.7. W₁ X₁ W₂ X₂ 0 W3 W₂₁ X3 +1 Apply the perceptron learning rule with the above input and with desired output 0. What are the new weights after this update ? Note: The output 0 here is fixed and may or may not be the answer to the previous question.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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