The purpose of this post is to summarize the content of cs231n lecture for me, so it could be a little bit unkind for people who didn’t watch the video . In addition, I omitted some contents that I don’t think it’s important enough, so use this article as just an assistance. Loss Function Below is the general numerical expression of loss functions. Multiclass SVM Loss There are many many many kinds of loss functions, but this class only deals with two of them : SVM loss and softmax . Above is the expression of SVM loss function. Meaning of the expression is, that score of the correct class should be larger at least one comparing to scores of the others to make the loss zero. It is more easier to understand it with an example. First, get each loss of the class like the image above. I'm not omitting the explanation because it's annoying to write. It's because explanation of the lecture slide is kind enough. And then, get the loss by calculating the mean va
The purpose of this post is to summarize the content of cs231n lecture for me, so it could be a little bit unkind for people who didn’t watch the video . In addition, I omitted some contents that I don’t think it’s important enough, so use this article as just an assistance. Prologure Obstacles for Image Classfication For below reasons! Obstacle is that cats are too cute..! There Is No Magic in Image Classification There is no function like below. def classify_image (image) : # Some magic here? return class_label Instead, image classification functions follow these two steps. function1 : inputs images, outputs model function2 : inputs model, predicts images Algorithms for Image Classification Simple Nearest Algorithm It’s literally simple. All you have to do to use this algorithm is just to calculate the mean value of the gap of each spot. If the calculated mean value is low, this algorithm says the two images are similar. Limitation K-near