👉 Activation math, also known as activation function math, is a fundamental component in neural networks that determines whether a neuron should be activated or not based on the input it receives. It involves applying a specific mathematical function to the weighted sum of inputs and biases within a neuron, known as the activation function. This function transforms the input into an output signal that is passed to the next layer of neurons, effectively introducing non-linearity to the model. Common activation functions include the sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU), each with its own characteristics that influence the learning process and model performance. The choice of activation function can significantly impact how well a neural network learns complex patterns in data, making it a crucial aspect of model design.
