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Consider the following neural network. Single-circled nodes denote variables (e.g. x₁ is an input variable, hy is an intermediate variable, ŷ is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and a denotes the logistic function o(x) = 1+-. In the network below, h₁ = 1+e-ª1w1-x2 w₂

Consider the following neural network. Single-circled nodes denote variables (e.g. x₁ is an input variable, hy is an intermediate variable, ŷ is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and a denotes the logistic function o(x) = 1+-. In the network below, h₁ = 1+e-ª1w1-x2 w₂

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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2: Back propagation: Consider the following neural network. Single-circled nodes denote variables (e.g. æ1 is an input variable, hy is an intermediate variable, y is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and ♂ denotes the logistic function σ(x) = 1+e-x. In the network below, hi = l+e-T1w1T2W2 · 1 1 W1 = -1.7 Cloon Σ W2 = 0.1 1 h - 0.6 oblood Σ S2 W4 = - 1.8 W5 = ww 0.2 = 0.5 3
Transcribed Image Text:2: Back propagation: Consider the following neural network. Single-circled nodes denote variables (e.g. æ1 is an input variable, hy is an intermediate variable, y is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and ♂ denotes the logistic function σ(x) = 1+e-x. In the network below, hi = l+e-T1w1T2W2 · 1 1 W1 = -1.7 Cloon Σ W2 = 0.1 1 h - 0.6 oblood Σ S2 W4 = - 1.8 W5 = ww 0.2 = 0.5 3
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