What term refers to the connections between neurons that carry numbers and determine influence?

What term refers to the connections between neurons that carry numbers and determine influence?

• A. Input Layer
• B. Hidden Layers
• C. Weights
• D. Output Layer

Answer: (C)

In a neural network, the connections between neurons are defined by weights—the numerical values assigned to each connection. These weights determine how strongly a signal from one neuron influences the next one. A larger weight means a stronger influence, while a smaller weight means a weaker impact. During training, the network adjusts these weights to reflect how important each input is for the task, shaping the overall behavior and predictions. Think of it this way: when a neuron fires, the inputs it receives are multiplied by their respective weights before being summed and passed through an activation function. The weights are the numbers that carry influence through the network, encoding which inputs matter most for the final output. The other terms describe different parts of the network: the input layer simply accepts data, hidden layers perform computations, and the output layer produces the result.

In a neural network, the connections between neurons are defined by weights—the numerical values assigned to each connection. These weights determine how strongly a signal from one neuron influences the next one. A larger weight means a stronger influence, while a smaller weight means a weaker impact. During training, the network adjusts these weights to reflect how important each input is for the task, shaping the overall behavior and predictions.

Think of it this way: when a neuron fires, the inputs it receives are multiplied by their respective weights before being summed and passed through an activation function. The weights are the numbers that carry influence through the network, encoding which inputs matter most for the final output. The other terms describe different parts of the network: the input layer simply accepts data, hidden layers perform computations, and the output layer produces the result.