An Artificial Neural Network (ANN) is a supervised learning algorithm that is loosely inspired by the way the human brain functions. Similar to the way neurons are connected and activated in the human brain, a neural network takes input and passes it through a function, resulting in certain subsequent neurons getting activated, and consequently producing the output. There are several standard ANN architectures. The universal approximation theorem says that we can always find a large enough neural network architecture with the right set of weights that can exactly predict any output for any given input. This means, for a given dataset/task we can create an architecture and keep adjusting its weights until the ANN predicts what we want it to predict.

By offering powerful image and video processing algorithms, Caer provides both casual and advanced users with an elegant interface for Machine vision operations. It leverages the power of libraries like OpenCV and Pillow to speed up your Computer Vision workflow -- making it ideal if you want to quickly test out something. This design philosophy makes Caer ideal for students, researchers, hobbyists and even experts in the fields of Deep Learning and Computer Vision to quickly prototype deep learning models or research ideas. Caer is an alternate Computer Vision library in Python that's designed to help speed up your Computer Vision workflow. It's ideal for rapid prototyping so you can focus more on the experimenting rather than the building. I use this package every single day when working on image and video processing workflows and it saves me tons of time!

In the last 50 years, computers have learned to count and classify but still weren't able to see until now. Today, as of 2019, the field of computer vision is rapidly flourishing, holding vast potential to alleviate everything from healthcare disparities to mobility limitations on a global scale. In recent years, we have seen great success in Computer Vision built on top of AlexNet or similar CNN based architectures as a backbone. It's true that the process is modeled after the human brain in terms of how it learns; a network of learning units called neurons learn how to convert input signals such as a picture of a house into corresponding output signals like the label'house'. For more details regarding this see my previous blog.

In the previous blog, I discussed Visual Perception and its both biological and computational aspects. This blog is specifically about computational Visual Perception, also known as Computer Vision. Computer vision has been around for more than 50 years, but recently, we see a major resurgence of interest in how machines'see' and how computer vision can be used to build products for consumers and businesses. The key driving factor behind all these is Computer Vision. In the simplest terms, Computer Vision is the discipline under a broad area of Artificial Intelligence which teaches machines to see.