Softmax and cross-entropy for multi-class classification.

Let's say you're trying to classify animals in a jungle. You have pictures of different animals like lions, tigers, bears and monkeys. And you want to use a computer program to tell which animal is in the picture.

The Softmax function is a way for the computer to decide which animal is in the picture. It looks at all the different possibilities (like lion, tiger, bear, or monkey) and assigns a probability to each one. For example, if the computer thinks there's a 80% chance the animal in the picture is a lion, a 10% chance it's a tiger, a 5% chance it's a bear and 5% chance it's a monkey, it would use the Softmax function to turn those chances into probabilities.

Cross-entropy is a way to measure how well the computer is doing at classifying the animals. It compares the probabilities that the computer came up with to the true answer (like which animal is really in the picture). If the computer's probabilities are close to the true answer, the cross-entropy will be low. But if the computer's probabilities are very different from the true answer, the cross-entropy will be high.

Softmax and Cross-entropy are commonly used together in a multi-class classification problem, where the goal is to identify which class an input belongs to. Softmax function is used to transform the output of a model into a probability distribution over all the classes and Cross-entropy is used as a loss function to measure how well the predicted probability distribution matches the true labels.

So in short, Softmax is a way for the computer to turn its guesses into probabilities and cross-entropy is a way to measure how well the computer's guesses match the true answer. Together, they help the computer to learn and improve its ability to classify animals correctly.

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