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.
Art critics have been present long before the birth of photography and have accompanied photographers through the journey from analog to digital. Now, with the proliferation of machine learning and the integration of on-device ML chips, such as Apple's Neural Engine chip, your smartphone has evolved into a discerning critic of your photographic creations.
Have you ever wanted to caption your photos automatically? With the GPT-3 Davinci model from OpenAI, you can do just that! By using image keywords, people, locations, and the album name, you can use AI/ML to generate captions that are not only descriptive, but also entertaining (and frequently hilariously wrong).
In this post, I’ll explore the capabilities of GPT-3 for writing captions based on image data, and how it can add a new dimension to your photos.
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.