# Computer Vision Computer Vision is one cool application of machine learning techniques. CV is all about making computer programs recognize images and videos, and doing things that humans can do normally, such as tracking objects or detecting faces. ## How can Machine Learning help? Machine learning is useful for many different computer vision tasks, as we can train a classifier to do whatever task we need to do. For example, image classification is a very important computer vision task. To solve this problem of tagging images to objects, we can use a deep learning technique (mentioned in a previous page). ImageNet is an example of a gigantic dataset (14 million images) that can be used for this task. Because of its sheer size, ImageNet is typically used in Computer Vision problems. ## What are some more applications of CV? Other applications include facial recognition, edge detection and more! For more information, check out the CV talk. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://15-112.gitbook.io/machine-learning/computer-vision.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.