What is a sigmoid function?
A mathematical function that "squishes" an input value into a constrained range, typically 0 to 1 or -1 to +1.
sigmoid function explained in plain English
A mathematical function that "squishes" an input value into a constrained range, typically 0 to 1 or -1 to +1. That is, you can pass any number (two, a million, negative billion, whatever) to a sigmoid and the output will still be in the constrained range. A plot of the sigmoid activation function looks as follows: The sigmoid function has several uses in machine learning, including: - Converting the raw output of a logistic regression or multinomial regression model to a probability. - Acting as an activation function in some neural networks.
The sigmoid function over an input number x has the following formula:
In machine learning, x is generally a weighted sum. ---
Example
Practitioners refer to sigmoid function when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- Bayesian neural network
A probabilistic neural network that accounts for uncertainty in weights and outputs.
- embedding layer
A special hidden layer that trains on a high-dimensional categorical feature to gradually learn a lower dimension embedding vector.
- full softmax
Synonym for softmax.
- generative model
Practically speaking, a model that does either of the following: - Creates (generates) new examples from the training dataset.
- gradient boosting
A training algorithm where weak models are trained to iteratively improve the quality (reduce the loss) of a strong model.
- Gradient Descent
The method by which a model gradually improves by making small adjustments after each mistake, moving toward better performance.
- input layer
The layer of a neural network that holds the feature vector.
- logistic regression
A type of regression model that predicts a probability.
- minimax loss
A loss function for generative adversarial networks, based on the cross-entropy between the distribution of generated data and real data.