Activation

Activation in neural networks refers to the process of determining whether a neuron should be "fired" or activated based on its input. Think of it like a threshold: if the input signal is strong enough, the neuron produces an output signal; if not, it remains inactive. This process helps the network learn complex patterns by enabling it to decide which information is important to pass through and which to ignore. Activation functions, such as ReLU or sigmoid, define how this "firing" happens, allowing the network to model intricate relationships within data.