18 Questions
What is the reason for the validation-sample error not evolving smoothly with the number of epochs?
Local minima in the error surface
What is the benefit of selecting a slower stopping rate in the presence of local minima?
Improved generalization performance
What is the primary function of feature extraction in convolution networks?
To extract local features
What is the primary advantage of convolution networks?
High degree of invariance to translation, scaling, and skewing
What is the primary limitation of the early-stopping method?
It is sensitive to the choice of stopping criteria
What is the primary benefit of using cross-validation in the early-stopping method?
Improved generalization performance
What happens to the local gradient when weights are assigned small initial values?
It becomes very small and causes learning to slow down.
Why is the origin a saddle point on the error surface?
Because the curvature is positive across the error surface and negative along it.
What is the advantage of setting the standard deviation of the induced field between the linear and saturated parts of the sigmoid?
It allows the neuron to operate in the linear region of the sigmoid.
What is the assumption made about the inputs applied to the MLP?
They have zero mean and unit variance.
Why should weights not be too large or too small?
Because it can cause the neuron to operate in the saturated region of the sigmoid.
What is the purpose of initializing the synaptic weights from a uniformly distributed set of numbers with zero mean?
To ensure the neuron operates in the linear region of the sigmoid.
What is the primary goal of early-stopping method of training?
To achieve good generalization by preventing overfitting
What happens to the MSE as the number of epochs increases?
It decreases
What is done periodically during the early-stopping method of training?
The validation error is measured
What can be inferred from the shape of the validation curve?
The model is overfitting
How often is the estimation (training) interrupted during the early-stopping method of training?
Every five epochs
What is the purpose of the validation subset in the early-stopping method of training?
To test the model's generalization capabilities
Learn about the importance of initializing weights in the backpropagation algorithm, and how it affects the learning process. Understand the impact of large and small weights on the error surface.
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