Binary Cross-Entropy Loss
Binary Cross-Entropy (BCE), also known as log loss, is a loss function used in binary or multi-label machine learning training.
It's nearly identical to Negative …
Binary Cross-Entropy (BCE), also known as log loss, is a loss function used in binary or multi-label machine learning training.
It's nearly identical to Negative …
Categorical Cross-Entropy Loss Function, also known as Softmax Loss, is a loss function used in multiclass classification model training. It applies the Softmax Activation Function …
Cross-entropy measures the average number of bits required to identify an event if you had a coding scheme optimised for one probability distribution $q …$
Entropy is a measure of uncertainty of a random variable's possible outcomes.
It's highest when there are many equally likely outcomes. As you introduce more …
The Softmax Activation Function converts a vector of numbers into a vector of probabilities that sum to 1. It's applied to a model's outputs (or …
The Sigmoid function, also known as the Logistic Function, squeezes numbers into a probability-like range between 0 and 1.^{1} Used in Binary Classification model …
To prevent cheating, a game needs a rule enforcer.
"if players feel like your game can be cheated, some will try to cheat, but most …