Weight
Definition
A parameter within a neural network that transforms input data within the network's hidden layers.
Deep Dive
In the context of neural networks and machine learning, a "weight" is a learnable parameter that determines the strength or importance of a connection between two neurons (nodes) or between an input feature and a neuron. Each input to a neuron is multiplied by its corresponding weight, and these weighted inputs are then summed along with a bias term before being passed through an activation function. The model learns by iteratively adjusting these weights during the training process, typically through optimization algorithms like backpropagation and gradient descent, aiming to minimize the difference between its predictions and the actual target outputs.
Examples & Use Cases
- 1In an image recognition model, a weight associated with a specific pixel might be high if that pixel's intensity is critical for identifying a particular object.
- 2In a simple linear regression model, the slope of the line is a weight, indicating how much the output changes for a unit change in input.
- 3Adjusting the weights between layers in a deep neural network to reduce the prediction error for classifying customer sentiment in text.