What is the primary focus of the term 'weights' in neural networks?

The term 'weights' in neural networks primarily refers to the parameters that determine the strength and significance of connections between neurons. Each connection between neurons has an associated weight that influences how much input from one neuron will affect the next. When a neural network is trained, these weights are adjusted through algorithms such as backpropagation, allowing the network to learn from the data it processes.

The adjustment of weights enables the model to capture and represent complex patterns in the data, facilitating better predictions or classifications. As a result, weights play a critical role in dictating how information is processed within the network, ultimately impacting the network's performance on tasks such as image recognition, natural language processing, and more.

In contrast, while reducing complexity, adjusting biases, and increasing the number of neurons may play roles in the overall architecture or functioning of a neural network, they do not define the specific focus of the term 'weights.' Thus, the primary focus of 'weights' is indeed to determine the connection strength between neurons.