Activation functions in neural networks are crucial because they introduce non-linearity into the model. Without activation functions, a neural network would essentially behave like a linear regression model, no matter how many layers it has. By applying non-linear functions like ReLU, Sigmoid, or Tanh, the network can learn complex patterns and make better predictions.
Activation functions also control the flow of information within the network, helping it decide when to activate a particular neuron based on the input it receives. For example, ReLU activates a neuron only if the input is positive, helping the model learn sparse representations.
In short, activation functions allow neural networks to approximate a wide range of functions, enabling them to solve more complex tasks like image recognition, language translation, and game playing.