Computer vision is a branch of artificial intelligence that allows computers and systems to extract valuable information from digital images, videos, and other visual inputs and then act or make suggestions based on that information.The insights gained from computer vision are then used to take automated actions. Just like AI gives computers the ability to ‘think’, computer vision allows them to ‘see’. The technology has its roots in the early 1950s, when pioneering researchers began exploring ways to teach computers to recognize simple visual patterns. Today, computer vision is used in a wide range of applications, including facial recognition, object detection, and autonomous vehicles.
The science and functionality of computer vision:
Computer vision is powered by two key technologies: a convolutional neural network and deep learning, a type of machine learning.
Through the use of algorithm-based models, machine learning enables computers to understand context through the visual examination of data. The model will be able to "see the broad picture" and distinguish between visual inputs after it has been given enough data. The computer employs AI algorithms to learn independently rather than being taught to recognize and distinguish between images.
By dividing images into pixels, convolutional neural networks enable machine learning models to see. A label or tag is assigned to each pixel. Then, using all of these labels, convolutions—a mathematical operation that combines two functions to yield a third function. Convolutional neural networks can handle visual inputs in this way.
Few major examples of Computer vision-
The largest data science community in the world, Kaggle offers you strong tools and resources to assist you in achieving your data science objectives. Users of Kaggle can discover and share data sets, study and develop models in a web-based data science environment, collaborate with other data scientists and machine learning experts, and participate in competitions to address data science challenges.
You can display your finished projects, including your datasets, Jupyter notebooks, and project reports, on your public profile as you take part in Kaggle competitions.
Your data science work can be displayed on GitHub, which is a highly valuable site. You must employ GitHub as your first platform as a repository for finished projects as you progress in the field of data science. These exercises could be capstone projects or weekly assignments. You must share
your code on this site with other data scientists and ambitious data scientists. Therefore, it's critical that you create a really robust and expert GitHub portfolio.
Ensure your repository's title is appropriate. After that, add a README file with a brief description of your project. Then after, you can upload the dataset, Jupyter notebook, and sample outputs of your project.
LinkedIn is a highly effective tool for networking with other data science professionals and companies as well as for showing your talents. One of the most well-known sites for advertising and attracting data scientists is LinkedIn.
Always make sure your profile is updated. List your experience, including the projects you've undertaken, and your skill sets in data science. It would be beneficial to include a list of honors and awards. Additionally, you should let recruiters know that you are searching for employment right now. You should also follow publications and data science influencers on LinkedIn to remain updated.
Joining the Medium community as a follower is a great way to connect with other data science experts. You can also follow particular Medium writings with a data science focus.
Writing on Medium has many benefits, you can use it as a platform to demonstrate your data science expertise. It also encourages you to work on difficult data science projects, which helps you develop your data science abilities.