What isActivation Function
A mathematical function applied to the weighted sum of inputs in a neural network to determine the output of a neuron. It introduces non-linearity into the network, enabling it to learn complex patterns.
An activation function is a crucial component in artificial neural networks. It's a mathematical function applied to the weighted sum of inputs in a neuron to determine the output.
This function introduces non-linearity into the network's operation, which is essential for learning complex patterns and relationships in data. Without activation functions, the network would just be a linear function, severely limiting its capacity to model intricate data structures.
Importance of Non-linearity
Non-linearity enables the network to learn complex relationships in data that cannot be captured by linear models. This is vital because many real-world problems involve intricate, non-linear patterns.
- Classifying images, Predicting stock prices, Recognizing speech, Generating text
These examples highlight the critical role of activation functions in allowing neural networks to tackle complex real-world tasks.