Supervised learning algorithms are super useful when it comes to recognizing images.
They learn from data that is already labeled, which helps them pick out patterns in pictures. Here’s how they work:
Training Phase: First, you show the algorithm a lot of labeled pictures (like pictures of cats and dogs). Each picture comes with a label that tells the computer what it is.
Feature Extraction: The algorithm looks for important features in the pictures, like shapes or colors. This helps it tell the difference between categories.
Prediction: After it has been trained, when a new picture comes in, the model can guess its category based on what it has learned.
Real-World Examples: Supervised learning is used in things like facial recognition, medical imaging, and even in self-driving cars. This makes our lives a bit easier and more efficient!
Supervised learning algorithms are super useful when it comes to recognizing images.
They learn from data that is already labeled, which helps them pick out patterns in pictures. Here’s how they work:
Training Phase: First, you show the algorithm a lot of labeled pictures (like pictures of cats and dogs). Each picture comes with a label that tells the computer what it is.
Feature Extraction: The algorithm looks for important features in the pictures, like shapes or colors. This helps it tell the difference between categories.
Prediction: After it has been trained, when a new picture comes in, the model can guess its category based on what it has learned.
Real-World Examples: Supervised learning is used in things like facial recognition, medical imaging, and even in self-driving cars. This makes our lives a bit easier and more efficient!