Before deep learning, creating computer vision algorithms that could process medical images required extensive efforts from software engineers and subject matter experts.

Duration: 4h In this module, you will learn about the field of Computer Vision. Computer Vision has the goal of extracting information from images. We will go over the major categories of tasks of ...

AI’s revolution lies in the integration of automation, big data, computer vision and deep learning, forming the essential ABCD pillars that are reshaping lives.

From super-resolution smartphone cameras to vehicles that can anticipate human movement, computer vision is undergoing a transformation—and AI is at its core. As deep learning continues to mature, its ...

Deep learning hasn't (yet) rendered classical computer vision obsolete. Why some challenges are still best solved with traditional algorithms.

Like most deep-learning networks, CNNs are organized in layers. In the lower layers, they learn simple shapes and edges from the images.

Deep learning's availability of large data and compute power makes it far better than any of the classical machine learning algorithms.

Deep Learning uses multiple layers of neural networks to extract complex features from data, enabling breakthroughs in computer vision, speech recognition, and other AI applications.