Consider a neural network with two input nodes ¤1 and x2, two hidden nodes h1 and h2 and one output node o. All nodes have sigmoid activation functions. The weights for the connections are as follows:
Consider a neural network with two input nodes ¤1 and x2, two hidden nodes h1 and h2 and one output node o. All nodes have sigmoid activation functions. The weights for the connections are as follows:
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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![Neural Networks and Gradient Descent
17. Consider a neural network with two input nodes x1 and x2, two hidden nodes h1 and h2
and one output node o. All nodes have sigmoid activation functions. The weights for the
connections are as follows:
Connection Weight
xi → hi
0.5
X1 → h2
X2 → hj
X2 → h2
hi → o
-0.9
0.1
-0.7
0.8
h2 → o
0.1
Table 3: Neural Network Weights](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff6129787-5418-4877-9c09-052154f3d92f%2F2544767e-15aa-43f1-84c5-f45413c9cd05%2Fuozryfn_processed.png&w=3840&q=75)
Transcribed Image Text:Neural Networks and Gradient Descent
17. Consider a neural network with two input nodes x1 and x2, two hidden nodes h1 and h2
and one output node o. All nodes have sigmoid activation functions. The weights for the
connections are as follows:
Connection Weight
xi → hi
0.5
X1 → h2
X2 → hj
X2 → h2
hi → o
-0.9
0.1
-0.7
0.8
h2 → o
0.1
Table 3: Neural Network Weights
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