What Stops Neural Networks from Becoming Linear Models
Towards AI
•
Machine Learning
Understanding activation functions, ReLU, GELU, Softmax and the role of non-linearity in deep learning Deep neural networks are built from surprisingly simple mathematical components. One of the most important is the activation function - the mechanism that allows neural networks to escape linearity and model complex patterns. Without activation functions, even extremely deep networks would collapse into simple linear transformations.