A Loss Function, also known as a cost function, is used in machine learning to measure how well a model is doing its job.

Think of it like a game of darts. Your goal is to hit the bullseye. Each time you throw a dart, you can measure how far off you are from the bullseye. The closer you are, the better your throw. The loss function is like that measurement. It tells us how far off our model’s predictions are from the actual values.

In machine learning, we want our model’s predictions to be as close as possible to the actual values. The loss function measures the difference between the model’s predictions and the actual values. The smaller the difference, the better the model is doing.

Different types of loss functions are used for different types of tasks. For example, Mean Squared Error is a common loss function for regression tasks, where the goal is to predict a continuous value. Cross-Entropy Loss is often used for classification tasks, where the goal is to predict which category something belongs to.

The goal in training a machine learning model is to find the model parameters that minimize the loss function. This is often done using methods like gradient descent and backpropagation.

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