CS 451 Quiz 14
Large scale machine learning; SVM cost function
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What is a typical training set size for a modern "large dataset"?
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1 point
m = 200,000
m = 5,000,000
m = 100,000,000
How can you tell that training with a large data set will give better performance than when training with just a small subset (m = 1000) of the data?
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1 point
If the learning curves (training and validation costs as a function of m) indicate high bias for small m
If the learning curves (training and validation costs as a function of m) indicate high variance for small m
Batch gradient descent means to make a single gradient descent steps after looking at
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1 point
one training example
several/many training examples
all training examples
For large training sets, stochastic gradient descent can be much faster than batch gradient descent
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1 point
True
False
In minibatch gradient descent, a typical choice of the minibatchsize b is
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1 point
b = 10
b = sqrt(m)
b = m/10
In order to check stochastic gradient descent for convergence, we can compute the average of, say, the last 1000 cost values. For each training example, the cost value should be computed
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1 point
before making the gradient descent step
after making the gradient descent step
In order for stochastic gradient descent to converge, it can be a good idea to decrease the learning rate with the number of iterations.
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1 point
True
False
The cost function used in SVMs is similar to the cost function in logistic regression, but instead of using the log function
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1 point
it is piecewise linear
it is quadratic
it uses the tan function
In logistic regression, we have a parameter lambda controlling the amount of regularization. In SVMs, what do we have instead?
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1 point
A parameter C that acts like lambda
A parameter C that acts like 1 / lambda
SVMs don't require regularization
SVM stands for
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1 point
System Validation Model
Singular Value Manipulation
Special Value Machine
Support Vector Manipulation
Support Vector Machine
Student Volunteer Movement
Space Vector Modulation
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