Stanford CS231N | Spring 2025 | Lecture 2: Image Classification with Linear Classifiers

We will be talking today about image classification . Basically continuing our our discussion on the topic of image classification from last uh lecture and we'll get a little bit into some uh topics that gets us closer to neural networks and and ultimately convolutional neural networks and so on. . We'll start with linear classifiers . Moving to the next slide. This was the the syllabus that we've talked about last uh lecture in the in the previous lecture where we did talk about three major categories of topics . Deep learning basics, perceiving uh and understanding the But this is what we will be covering . So what is image classification ? Given an image and a number of predefined labels , predetermined labels, a set of possible labels such as in this example you see dog, cat, truck, plane, and so on . The job of the system is to assign one of those labels to this uh image . So to us this is actually a very very easy task because our brains our co cognitive system is wired to get a holistic understanding of this image and assign a label to it. But . But when it comes to coding this and and looking at how a computer can make sense of this image that's a that's completely a different story and we want to see how machines can make . So images are of . So images are of . So images are of . So images are often defined by matrices of data more broadly more generally tensors of data and often the numbers are the each of the pixel values are between zero and 255 which is a 8 bit um data structure and since this is an image a colored image assuming that it's with a resolution of 800 by 600 . Since it's an R RGB image, it has three channels of red, green, and blue RGB.