Activation Functions: The Secret Sauce Behind Neural Networks
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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
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Insightforge | AI & Data Science on Instagram: "Imagine you’re working with a dataset containing two classes, but the data isn’t linearly separable. In such cases, simple linear models like logistic regression or linear SVMs struggle to create an accurate decision boundary. This is where neural networks excel. By introducing nonlinearity through activation functions such as ReLU, sigmoid, or tanh, they transform the input space layer by layer, allowing the model to learn much more complex patter
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