Activation Functions
Deep Learning
Mathematical functions that determine the output of a neural network node based on its input
Detailed Explanation
Activation functions are mathematical functions applied to a neural network node's input to introduce non-linearity, enabling the network to learn complex patterns. They transform the weighted sum of inputs into an output, which is then passed to subsequent layers. Common examples include ReLU, sigmoid, and tanh, each influencing the network's ability to model various data relationships.
Use Cases
- •Activation functions enable neural networks to learn complex patterns by introducing non-linearity, crucial for tasks like image recognition and natural language processing.
Related Terms
Other terms in the Deep Learning category