🎨 Welcome to the Activation Zoo
Discover the functions that bring neural networks to life—introducing non-linearity for complex pattern learning
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0 / 5 completedWhy Activation Functions?
Without activation functions, neural networks would just be linear transformations. No matter how many layers you stack, the result would be equivalent to a single linear layer.
🎯 The Non-Linearity Problem
Linear transformations can only create straight decision boundaries. Real-world problems need curves, circles, and complex shapes.
Linear Layers
Perform weighted sums of inputs. Essential for learning, but can't model complex patterns alone.
Activation Functions
Add non-linearity, enabling networks to learn complex decision boundaries and patterns.
Key Properties
Universal Approximation Theorem
Neural networks with at least one hidden layer and non-linear activation functions can approximate any continuous function to arbitrary precision (given enough neurons).