Understanding Linear and Non-linear Activation Functions in Deep Learning
Understanding linear and non-linear activation functions is crucial in deep learning. Linear functions maintain a simple relationship between input and output, suitable for regression tasks. Non-linear functions like ReLU, sigmoid, and tanh introduce complexity, enabling networks to capture intricate patterns in the data. grasping these distinctions is essential for effective model design and optimization.
Understanding Linear and Non-linear Activation Functions in Deep Learning Read More »
