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Sameer GuptaGuest
For one of our college projects we were using Bayesian network earlier. Now, the group lead has asked us to perform similar tasks using Artificial Neural Network instead of Bayesian Network. So, I wanted to know about the difference between Bayesian Network and Artificial Neural Network.
I would then plan to migrate things after knowing the difference in both of these networks.
Artificial neural networks (ANNs) and Bayesian networks are two different types of machine learning models that are used for different purposes.
An ANN is a type of machine learning model that is inspired by the structure and function of the human brain. It consists of a large number of interconnected processing units (neurons) that are able to learn and adapt to new data. ANNs are commonly used for tasks such as image recognition, language translation, and predictive modeling.
A Bayesian network, on the other hand, is a probabilistic graphical model that represents the relationships between different variables and their probabilities. It is used to model uncertain or probabilistic systems and can be used for tasks such as predicting the likelihood of certain events or making decisions based on uncertain data.
In summary, ANNs are used for tasks such as image recognition and language translation, while Bayesian networks are used to model uncertain systems and make decisions based on probabilistic data.