Concepts4
📚TheoryAdvanced
Deep Learning Generalization Theory
Deep learning generalization theory tries to explain why overparameterized networks can fit (interpolate) training data yet still perform well on new data.
#generalization#implicit regularization#minimum norm+12
📚TheoryAdvanced
Statistical Learning Theory
Statistical learning theory explains why a model that fits training data can still predict well on unseen data by relating true risk to empirical risk plus a complexity term.
#statistical learning theory#empirical risk minimization#structural risk minimization+11
📚TheoryAdvanced
VC Dimension
VC dimension measures how many distinct labelings a hypothesis class can realize on any set of points of a given size.
#vc dimension#vapnik chervonenkis#shattering+12
📚TheoryAdvanced
Rademacher Complexity
Rademacher complexity is a data-dependent measure of how well a function class can fit random noise on a given sample.
#rademacher complexity#empirical rademacher#generalization bounds+12