Weighing in on weights and biases

Weights and biases are some of the most important components of NNs. Their functionality within NN nodes complements each other, similar to how weights and biases fit linear regression models. Understanding weights and biases will help you understand how they transform an NN from a static structure into a dynamic learning system. Proficiency in initializing, updating, and optimizing these components is essential in the journey of training NNs effectively.

Introduction to weights

Weights are numerical values that are assigned to the connections between neurons. Each connection possesses a corresponding weight value, which dictates the strength of the influence one neuron has on another. During training, these weights are adjusted, enabling the network to capture patterns and relationships within the data it processes.

Initially set to random values, these weights are fine-tuned through techniques such as backpropagation and gradient descent...