The Bernoulli distribution is a probability distribution that represents the outcome of a single binary event, such as a coin flip. It is characterized by a single parameter, p, which represents the probability of the event occurring.
In TensorFlow and AI, the Bernoulli distribution is often used as a simple model for binary outcomes, such as predicting whether a user will click on an advertisement or whether a patient will develop a particular disease. It can also be used as a building block for more complex models, such as logistic regression or neural networks.
Some use cases for the Bernoulli distribution in TensorFlow and AI include:
Classification tasks: The Bernoulli distribution can be used to model the probability of a class label in a classification task. For example, you might use it to predict the probability that a patient has a particular disease based on their symptoms and test results.
Sampling: The Bernoulli distribution can be used to sample binary values, such as 0s and 1s, which can be useful for generating synthetic data or testing machine learning models.
Modeling binary outcomes: The Bernoulli distribution is often used as a simple model for binary outcomes, such as predicting whether a user will click on an advertisement or whether a stock will go up or down.