Activation functions play a crucial role in neural networks. Let’s dive into their purpose and impact:

1. Purpose of Activation Functions:
   • Non-Linearity: Activation functions introduce non-linearity to the network. Without them, the neural network would behave like a linear regression model, limiting its ability to capture complex data patterns.
   • Complex Phenomena: Non-linearity allows neural networks to approximate intricate functions. This enables tasks like language processing, image recognition, and time series predictions.
2. Impact on Learning Complex Patterns:
   • Transforming Data: Neural networks classify data that isn’t linearly separable. Activation functions transform data, making the resulting points linearly separable. This transformation enables the network to learn complex patterns.
   • Representation Power: By introducing non-linearity, activation functions allow neural networks to represent and learn intricate features, making them more adaptable to real-world phenomena.