Building a Model to Distinguish between Red and Green Apples
Imagine having a collection of 100 images of red and green apples, and your task is to create a model that can differentiate between them. This is what we call a supervised learning task, where the model is trained on labeled data to make predictions.
To start, we divide the dataset into 80% for training and 20% for testing. This means that 80 images will be used to train the model, while 20 images will be used to test its accuracy.
Training the model is like teaching a child to identify apples. You show the child a red apple and say, “This is red,” then show them a green apple and say, “This is green.” By repeating this process with 80 images, the model learns to distinguish between the two colors.
The key to improving the model’s performance is to provide it with more images. The more examples it sees, the better it becomes at recognizing patterns and making accurate predictions.
In conclusion, building a model to differentiate between red and green apples is a fascinating process that highlights the power of supervised learning in AI technology. With the right dataset and training, the possibilities are endless.