Applications of eigenvalues and vectors. 1. A neural network is not actually as smart as you think. Usually, it is simply using math to find the minimal amount of error possible. Suppose that the amount of error a neural network has is given by error = 5x + 12x1x2+ 5x + 2, x+ x% = 1 where xi and x2 are weights in the network (we impose the sum of the square of the weights is 1. This is a technique sometimes used to reduce overfitting). Find the minimal amount of error possible, and what weights correspond to that error.
Applications of eigenvalues and vectors. 1. A neural network is not actually as smart as you think. Usually, it is simply using math to find the minimal amount of error possible. Suppose that the amount of error a neural network has is given by error = 5x + 12x1x2+ 5x + 2, x+ x% = 1 where xi and x2 are weights in the network (we impose the sum of the square of the weights is 1. This is a technique sometimes used to reduce overfitting). Find the minimal amount of error possible, and what weights correspond to that error.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:Applications of eigenvalues and vectors.
1. A neural network is not actually as smart as you think. Usually, it is simply using
math to find the minimal amount of error possible. Suppose that the amount of error
a neural network has is given by
error = 5x + 12.x1x2 + 5x + 2, xỉ + x3 = 1
where xi and x2 are weights in the network (we impose the sum of the square of the
weights is 1. This is a technique sometimes used to reduce overfitting). Find the
minimal amount of error possible, and what weights correspond to that error.
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