Activation functions in neural networks: Types and examples

Algorithms | CS | Data Science on Instagram: "Neural networks need activation functions for one reason: without them, depth is fake. Remove activations and a “deep” network collapses into a single linear equation. Two layers or two hundred—it makes no difference. Linear models can only draw straight lines, and straight lines cannot model the real world. That means no vision, no speech, no language—no real AI. Activation functions introduce **non-linearity**, which gives neural networks expressiv