Loss Function
Mathematical function that measures the difference between predicted and actual values, guiding the learning process during training.
Key Concepts
Perception
The ability to interpret and understand sensory data from the environment, including vision, hearing, and other forms of input processing.
Reasoning
The capacity to process information logically, make inferences, and solve complex problems based on available data and learned patterns.
Action
The ability to execute decisions and interact with the environment to achieve specific goals and objectives effectively.
Learning
The capability to improve performance and adapt behavior based on experience, feedback, and new information over time.
Detailed Explanation
A loss function, also known as a cost or error function, is a fundamental pillar in the field of machine learning and deep learning. Its main purpose is to quantify the difference or "loss" between the values predicted by a model and the actual or "true" values. If the predictions are accurate, the value of the loss function will be low; if they are wrong, it will be high.
How Loss Functions Work
In a supervised learning problem, the model learns from a dataset that contains examples with their corresponding correct answers. For each example, the model generates a prediction. The loss function takes this prediction and compares it with the actual value, yielding a number that represents the error.
Loss functions must be differentiable, meaning that their derivative can be calculated. This is crucial because optimization algorithms use the gradient (the derivative) of the loss function to know in which direction they should adjust the model's parameters to reduce the error efficiently.
Real-World Examples & Use Cases
Regression Problems
In regression problems, the goal is to predict a continuous numerical value. Common loss functions include Mean Squared Error (MSE), Mean Absolute Error (MAE), and Huber Loss.
Classification Problems
In classification problems, the goal is to assign a label or category to an input. Common loss functions include Cross-Entropy Loss and Hinge Loss.