From facial recognition to automated driving — computer vision is a growing industry with lots of opportunities for people trained in computer science, robotics, or machine learning.
Whether you’re a recent college graduate, still in university or a self-studied newcomer, a computer vision internship represents a wonderful opportunity to learn more about the field. We’ll give you some tips on what to expect, and what you can do to get the most out of the experience.
With theaters and other public venues slowly reopening around the world, it is crucial that everyone adhere to mask-wearing to avoid further lockdowns. But how could we verify whether people really are following this rule? We could employ ushers or security personnel to act as enforcers — or we could build a computer vision application to check if everyone is wearing masks at all times, and which signals to us whenever someone isn’t.
This scenario is one of many examples of the uses of computer vision systems. In this article, we will show you how you can use APIs to embed computer vision in your own applications.
R might not be the first language that comes to mind when thinking about NLP. Short for “natural-language processing,” NLP is the discipline of making human language processable by computers. It is a growing field with thousands of applications, some of which you probably use in your daily life. Python has become the most popular language for researching and developing NLP applications, thanks in part to its readability, its vast machine learning ecosystem, and its APIs for deep-learning frameworks. However, R can be an equally good choice if you intend to quantify your language data for NLP purposes. Below, we list some of the most useful NLP libraries in R and walk through a simple NLP programming example.
If you’re at all keyed into artificial intelligence (AI) and machine learning (ML) concepts, chances are that you’ve heard of deep learning. It’s a fascinating topic, branching off of regular ML and delving into neural networks that attempt to mimic the way the human brain works. With deep learning, machines can practice unsupervised learning and figure out how to make decisions without direct guidance from a human.
Less than two decades ago, computer vision was considered a newer technology that was just starting to be adopted. Now, in 2020, computer vision usage continues to increase and is helping industries make strides in the areas of safety, productivity, accuracy and reliability, and more. This means there are more computer vision jobs available than ever before.
Find out more about what the job entails, the skills needed, salary details, and the industries where computer vision jobs are in the most demand.
Many of the modern computer vision applications rely on deep learning algorithms. Therefore, selecting and using datasets to train those deep learning algorithms is an essential skill for any computer vision engineer.
In this article, we’ll take a look at how computer vision datasets have refined the field of object identification and contributed to high-tech inventions like self-driving cars and the latest smartphones.
Robotic Process Automation (RPA) is the latest in futuristic innovation sweeping the tech industry and redefining business operations. Gartner analysts have predicted that RPA will be adopted by up to 50% of the Gulf Cooperation Council in the next two years. And with its growth, comes future-proof career opportunities for you across industries.
RPA is technology that will automate repetitive, monotonous, and error-prone tasks normally done by humans, freeing them up to do more innovative work that is stimulating, involves a higher level of thinking, and more impactful to the business.