Convolutional Neural Networks (CNNs) are a specific type of deep learning architecture that excels at tasks involving images, videos, and other grid-like data. They are particularly successful in applications like image classification, object detection, and image segmentation.
Imagine you're looking at a picture of a cat. Our brains are good at recognizing the shapes of ears, whiskers, and fur to tell it's a cat. Convolutional Neural Networks (CNNs) are like artificial brains for computers, especially good at understanding pictures and videos.
Here's the basic idea:
- CNNs look at images in small squares, like a detective examining a crime scene with a magnifying glass.
- They learn what patterns are important in each square, like edges, shapes, and colors.
- By putting these clues together, CNNs can eventually recognize the whole picture, just like you can recognize the cat.
This makes CNNs super useful for:
- Telling what's in a picture (cats, dogs, cars, etc.)
- Finding specific objects in a picture (like finding a cat in a photo of your living room)
- Understanding videos (like what's happening in a security camera recording)