Proving Linearity and Distribution of Ordinary Least Squares Estimator

Malkesh Dalia on Instagram: "Neural Networks: Why Activation Functions Matter A neural network without activation functions is just a stack of linear equations. No matter how many layers you add, it can only learn straight lines. Activation functions solve this. They introduce non-linearity, allowing the network to: • learn curves and complex patterns • separate data that isn’t linearly separable • model real-world phenomena like images, speech, and language In simple terms: Linear layers combin

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

Linear Functions