What essential role do weights and biases play in neural networks?

Weights and biases are fundamental components of neural networks, playing a critical role in guiding data processing throughout the network. Weights are parameters that adjust the strength of the input signals to the neurons, effectively determining how much influence each input feature has on the output. When data is fed into the network, these weights help in transforming the inputs into outputs by scaling the inputs accordingly.

Biases, on the other hand, provide an additional degree of freedom for the model. They allow the model to shift the activation function to the left or right, which can help in capturing the underlying patterns of the data more effectively. Together, weights and biases enable the neural network to learn complex functions from the training data by refining and optimizing the way information is processed at each layer.

Through the process of training, weights and biases are adjusted based on the errors produced by the model's predictions, facilitating the learning process by minimizing those errors. This dynamic adjustment is crucial for the network to improve its performance over time and accurately generalize to unseen data.