NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades.
Convolutional Neural Networks (CNNs) have become the go-to architecture for image recognition and computer vision tasks. CNNs excel at identifying patterns in images, such as edges, textures, and shapes, making them a key player in applications like image classification, object detection, and facial recognition. In this post, we'll explore the key components of CNNs, how they operate on images, and their use cases.
Have you ever stumbled upon a photo and found yourself wondering who that celebrity is, or what kind of object is in the background? Enter Amazon Rekognition, a powerful deep learning-based image and video analysis service that can help answer those questions.
NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.
Computer vision, the field of AI that enables computers to interpret and understand visual information from the world, has undergone significant advancements over the past decade. The ability to analyze images and videos, recognize objects, and understand visual scenes has opened up a multitude of applications in fields such as healthcare, autonomous vehicles, and security. In this blog post, we will explore the key milestones and breakthroughs that have shaped the evolution of computer vision over the last ten years.