Perceptron
Let's imagine you have a special toy called a "perceptron." This toy can learn how to recognize things, like different kinds of fruit.
First, you need to show the perceptron some examples of fruit. You could show it a banana, an apple, and an orange. Each example has different features that make it unique. For example, a banana is usually yellow and curved, while an apple is usually round and red or green.
When the perceptron looks at each example, it tries to figure out what features are important for recognizing that particular fruit. It does this by giving each feature a number - the more important the feature is, the higher the number. For example, the banana might get a high number for "yellow" and "curved", while the apple might get a high number for "round" and "red".
Once the perceptron has learned what features are important for each fruit, it can start recognizing new fruits. If you show it a picture of a green pear, for example, the perceptron will look at its features and use the numbers it has assigned to each feature to decide what kind of fruit it is.
The more examples you show the perceptron, the better it becomes at recognizing new things. As the perceptron learns, it can adjust the numbers it has assigned to each feature to make its recognition more accurate. So, in the future, it might give "yellow" a higher number for bananas than it did before, because it has seen more yellow bananas.
In a way, the perceptron is like a tiny robot scientist that is always learning and adapting to new information.
First, you need to show the perceptron some examples of fruit. You could show it a banana, an apple, and an orange. Each example has different features that make it unique. For example, a banana is usually yellow and curved, while an apple is usually round and red or green.
When the perceptron looks at each example, it tries to figure out what features are important for recognizing that particular fruit. It does this by giving each feature a number - the more important the feature is, the higher the number. For example, the banana might get a high number for "yellow" and "curved", while the apple might get a high number for "round" and "red".
Once the perceptron has learned what features are important for each fruit, it can start recognizing new fruits. If you show it a picture of a green pear, for example, the perceptron will look at its features and use the numbers it has assigned to each feature to decide what kind of fruit it is.
The more examples you show the perceptron, the better it becomes at recognizing new things. As the perceptron learns, it can adjust the numbers it has assigned to each feature to make its recognition more accurate. So, in the future, it might give "yellow" a higher number for bananas than it did before, because it has seen more yellow bananas.
In a way, the perceptron is like a tiny robot scientist that is always learning and adapting to new information.