What are Weights and Biases?
In machine learning, particularly in neural networks, weights and biases are the fundamental parameters that the model learns during training.
Weights:
Weights in a neural network are like volume knobs. They adjust how important each piece of input data is. When data goes through the network, each part of the data gets multiplied by its weight – just like turning the volume up or down on different parts of a song. This helps the network decide which parts of the input data to pay more or less attention to.
Biases:
Biases in a neural network are like little adjustments that help the model fit the data better. They allow the model to move its decision-making up or down, making predictions more accurate. Essentially, biases add a constant value to the input.