How Does All Hyperparameters of a Neural Network Explained Work?

A neural network is like a group of kids working together to solve a puzzle, and each hyperparameter is like a rule that tells them how to do it.

Imagine you're picking players for a soccer game. You decide how many kids are on the team (number of layers), how many players each position has (number of neurons), and how they pass the ball (activation function). These choices help your team play better, just like hyperparameters help your neural network learn faster or more accurately.

Some rules are about how much the kids should change their strategy after each game (learning rate). If it's too high, they might get confused; if it's too low, they take forever to win. Other rules say how many games they should play before taking a break (number of epochs), just like you might stop playing when you're tired.

Some kids remember the best moves from past games (batch size), and others use their own tricks to help everyone improve (regularization). All these hyperparameters are like custom rules that make sure your team plays its best, and wins more puzzles!