06-Addressing data mismatch _1647693141658.pdf
07-Transfer learning _1647693142950.pdf
08-Multi-task learning _1647693143866.pdf
10-Whether to use end-to-end deep learning _1647693144864.pdf
05-Bias and Variance with mismatched data distributions _1647693141079.pptx
06-Addressing data mismatch _1647693141877.pptx
07-Transfer learning _1647693143042.pptx
08-Multi-task learning _1647693143966.pptx
10-Whether to use end-to-end deep learning _1647693145001.pptx
02-Orthogonalization_1647693049822.pdf
03-Single number evaluation metric _1647693058877.pdf
04-Satisficing and Optimizing metric _1647693064019.pdf
06-Size of the dev and test sets _1647693073188.pdf
07-When to change dev:test sets and metrics _1647693079764.pdf
09-Avoidable bias _1647693096948.pdf
10-Understanding human-level performance _1647693116061.pdf
11-Surpassing human-level performance _1647693121807.pdf
12-Improving your model performance _1647693125832.pdf
03-Build your first system quickly, then iterate _1647693138822.pdf
04-Training and testing on different distributions _1647693139536.pdf
05-Bias and Variance with mismatched data distributions _1647693140969.pdf
01-Why ML Strategy _1647693040052.pptx
02-Orthogonalization_1647693054738.pptx
03-Single number evaluation metric _1647693059146.pptx
04-Satisficing and Optimizing metric _1647693064223.pptx
06-Size of the dev and test sets _1647693073346.pptx
07-When to change dev:test sets and metrics _1647693084568.pptx
09-Avoidable bias _1647693105544.pptx
10-Understanding human-level performance _1647693117414.pptx
11-Surpassing human-level performance _1647693121979.pptx
12-Improving your model performance _1647693126012.pptx
01-Carrying out error analysis _1647693136183.pptx
02-Cleaning up incorrectly labeled data _1647693137620.pptx
03-Build your first system quickly, then iterate _1647693138909.pptx
04-Training and testing on different distributions _1647693139829.pptx