TRUE OF FALSE 14) The sigmoid function uses the net amount of excitation as its argument. [ ] 15) The backpropagation algorithm is used to find the global minimum of the error function. [ ] 16) Normalization fumction is (Xi – min(X) / (Max (X) –Min(X)) [ ] 17) Neurons in the hidden layer can’t be observed through the input/output behaviour of the network. [ ] 18) Multi-layer feed-forward networks can learn any function provided they have enough units and time to learn [ ]
TRUE OF FALSE 14) The sigmoid function uses the net amount of excitation as its argument. [ ] 15) The backpropagation algorithm is used to find the global minimum of the error function. [ ] 16) Normalization fumction is (Xi – min(X) / (Max (X) –Min(X)) [ ] 17) Neurons in the hidden layer can’t be observed through the input/output behaviour of the network. [ ] 18) Multi-layer feed-forward networks can learn any function provided they have enough units and time to learn [ ]
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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Question
TRUE OF FALSE
14) The sigmoid function uses the net amount of excitation as its argument. [ ]
15) The backpropagation
16) Normalization fumction is (Xi – min(X) / (Max (X) –Min(X)) [ ]
17) Neurons in the hidden layer can’t be observed through the input/output behaviour of the network. [ ]
18) Multi-layer feed-forward networks can learn any function provided they have enough units and time to learn [ ]
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