Caltech Machine Learning Lecture 12
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Regularization putting the brakes on fitting noise.
Lecture 12 of 18 caltech's machine. Cs 485/685, university of waterloo. Augmented error weight decay.
Caltech's machine learning course (by professor yaser abumostafa) lecture 12. Model selection and data contamination. Lecture 18 caltech's course cs.
The most important theoretical result in machine learning. Validation taking a peek out of sample. Error and noise the principled choice of measures.
Machine learning tutorial caltech lecture 12 regularization. What happens when target we want to learn is noisy. Epilogue the map of machine learning.
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