The article critically examines the limitations of sigmoid functions in neural networks, particularly in the context of modern deep learning architectures. It discusses how sigmoids can lead to vanishing gradients and other optimization challenges, while comparing them to alternative activation functions like ReLU that have become more popular in practice.
Background
Sigmoid functions were once the standard activation function in neural networks but have been largely replaced by ReLU and its variants in modern deep learning due to better training dynamics and performance.
- Source
- Hacker News (RSS)
- Published
- May 15, 2026 at 06:51 PM
- Score
- 7.0 / 10