<ul><li>Activation functions in neural networks determine if a neuron should be activated based on the sum of its inputs and weights.</li><li>Activation functions enable neural networks to learn from non-linear data, allowing for complex mappings between inputs and outputs.</li><li>Three widely-used activation functions are Sigmoid, Tanh, and ReLU, each with its own strengths and weaknesses.</li><li>Choosing the right activation function is crucial for model performance, and Sigmoid, Tanh, and ReLU are commonly recommended based on specific requirements.</li></ul>

Activation Functions in Neural Networks: A Beginner’s Guide with Examples

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