1
Q
Bias variance trade off
A
2
Q
Common pitfalls of PCA
A
3
Q
Bias variance tradeoff math
A
4
Q
Favorite machine learning algorithm
A
5
Q
EIgen vector
A
For some nxn matrix A, x is an eigenvector of A if Ax = lamdba x where lambda is a scalr.
A matrix can represrnt linear transformation and when it is applied to a vector x, results in an eigen vector
6
Q
eigenvalue
A
Scaling value lambda
7
Q
eigendecomposition
A
decomposition of a squared matrix into its eigenvectors
8
Q
SVD
A
non-square matrices are decomposed using SVD
9
Q
Loss function
A
Tells us how well a model fits a particular dataset. The lower the loss, the more desirable.
10
Q
Optimization methods
A
Technoques for minimizing a cost function
aws machine learning
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