Note for CS231n
- Data preprocessing: Normalize the features in your data to have zero mean and unit variance before feed into the deep model.
- Split data tricks: This setting depends on how many hyperparameters you have and how much of an influence you expect them to have.
kNN defaults:
- remember all the data, i.e. store all the data into storage
- classify a single image cost much.(every classification need much computation)
2
Approach of neural network to do the classification task:
- score function: maps the raw data to class scores.
- loss function: quantify the agreement between the predicted scores.
cast as an optimization problem.
single matrix multiplication WxiWxi is effectively evaluating 10 separate classifiers in parallel where each classifier is a row of W.
classifying the test image involves a single matrix multiplication and addition, faster than comparing each test images to all training images which kNN use.
That’s also why we do like to use deep model although the training part is time-consuming, the test part is just a matrix multiplication and addition with complex O(n).
Depending on precisely what values we set for these weights,function has the capacity to like or dislike (depending on the sign of each weight) certain colors at certain positions in the image. That is to say the color have influence on the accuracy of classification.