Overview
Model weights are fundamental components of machine learning algorithms, acting as parameters that are adjusted during the training process to improve predictions. They play a crucial role in determining how input features influence the output of a model. Understanding how weights are adjusted, opti...
Key Terms
Example: In a neural network, weights determine how much influence an input has on the output.
Example: Gradient descent helps find the optimal weights for a model by iteratively reducing error.
Example: Backpropagation allows the model to learn from errors by adjusting weights accordingly.
Example: The mean squared error is a common loss function used in regression tasks.
Example: A model that performs well on training data but poorly on new data is overfitting.
Example: A linear model trying to fit a complex dataset may underfit the data.