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During the training process, weights and biases in each layer are adjusted using an optimization algorithm such as stochastic gradient descent that minimizes errors. The optimization iteratively updates weights and biases until errors have been eliminated from each layer.
During the training process, weights and biases in each layer are adjusted using an optimization algorithm such as stochastic gradient descent that minimizes errors. The optimization iteratively updates weights and biases until errors have been eliminated from each layer.
==Explain Like I'm 5 (ELI5)==
Layers in a machine learning algorithm are like building blocks. Think of them as different rooms in a house, each performing its own task such as counting or sorting on items given to it by its predecessor. Together these rooms work together to solve problems like finding answers to questions. The number and arrangement of rooms determines how effectively this house can solve issues.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
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Just as building a tower with many blocks makes it stronger, having multiple layers in a machine learning model enhances its capacity for understanding and making decisions.
Just as building a tower with many blocks makes it stronger, having multiple layers in a machine learning model enhances its capacity for understanding and making decisions.
==Explain Like I'm 5 (ELI5)==
Okay, so let me explain what a layer is in machine learning. Say you have pictures of animals like cats and dogs, and want to teach your computer how to distinguish between them.
Now the computer must learn to recognize certain features of these animals, like the shape of their ears or pattern on their fur.
That's where a layer comes in. It acts like an optical filter, looking at the pictures to pick out important details.
In other words, the first layer might focus on colors in a picture, while the second examines shapes, and finally, the third examines textures.
By analyzing all these different layers, the computer can learn to distinguish between a cat and dog just like you can!




[[Category:Terms]] [[Category:Machine learning terms]]
[[Category:Terms]] [[Category:Machine learning terms]]
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