Preeti
Activation functions are essential in deep learning as they introduce non-linearity to neural network layers, enabling them to learn complex tasks. This blog explores four common activation functions: Sigmoid, Tanh, ReLU, and PReLU. Each function is discussed in terms of its definition, mathematical formula, advantages, disadvantages, comparisons, and typical use cases in deep learning layers.
The Sigmoid function maps input values to a range between 0 and 1, often used in the output layer for binary classification. The Tanh function maps input values to a range between -1 and 1, preferred over Sigmoid for hidden layers to ensure zero-centered outputs, especially in recurrent neural networks.
ReLU, one of the most popular activation functions, outputs the input directly if positive; otherwise, it outputs zero. It is widely used in hidden layers of deep neural networks due to its simplicity and efficiency. PReLU, a variant of ReLU, addresses the dying ReLU problem by allowing negative slopes to be learned, offering more flexibility and adaptability in deep networks.
Choosing the right activation function is crucial for neural network performance. While Sigmoid and Tanh were once common, ReLU and its variants like PReLU are now preferred for mitigating the vanishing gradient problem and improving training efficiency. Understanding the strengths and limitations of each activation function helps in designing more effective and robust neural networks.
Source link
Source link: https://medium.datadriveninvestor.com/types-of-activation-functions-in-deep-learning-e7c2a48d3242
GIPHY App Key not set. Please check settings