Weights and biases are fundamental components of artificial neural networks, which are the backbone of many AI systems. These terms describe the parameters that the network adjusts during training to learn patterns and make predictions. Together, weights and biases enable the network to process inputs and produce outputs that approximate desired results, effectively forming the “knowledge” of the AI.

Weights are the values assigned to the connections between neurons in a neural network. They determine the strength and direction of the influence one neuron has on another. Imagine each connection as a pipe, with the weight controlling the flow of information, much like a valve. A higher weight amplifies the signal, while a lower or negative weight can diminish or reverse it. By tuning these weights during training, the network learns to emphasize or ignore certain inputs depending on their relevance to the task.

Biases are additional parameters that are added to the weighted sum of inputs before applying an activation function. They allow the model to adjust the output independently of the input values. This capability helps the network capture patterns that don’t pass through the origin point of a graph. Biases can be thought of as “nudges” that shift the function to better fit the data.

In training a neural network, algorithms like gradient descent adjust these weights and biases iteratively to minimize the error between predicted outputs and actual results. For instance, in an image recognition task, weights and biases are fine-tuned to detect specific features, like edges, shapes, and textures, that help classify the image correctly.

Altogether, weights and biases work as a dynamic team to mold how an AI system understands and processes information, forming the core of its decision-making processes.

Weights & Biases (W&B) is also the name of a popular machine learning platform that assists researchers and developers in tracking experiments, visualizing results, and collaborating on projects.