Bayesian machine learning has become increasingly popular because it can be used for real-world applications such as credit card fraud detection and spam filtering. This interesting topic came to us from Towards AI in their article, “Bayesian Inference: The Best 5 Models and 10 Best Practices for Machine Learning.”
This is of fundamental importance to the field of data science, consisting of the disciplines: computer science, mathematical statistics and probability. It is used to calculate the probability of an event occurring based on relevant existing information.
Many data scientists believe that combining probabilistic machine learning, Bayesian learning, and neural networks represents a potentially beneficial practice. However, it can be difficult to train a Bayesian neural network.
While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor take advantage of the well-studied tools of probability theory. This has started to change following recent developments of tools and techniques combining Bayesian approaches with deep learning.
Data Harmony is a fully customizable suite of software products designed to maximize precise, efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval and explainable artificial intelligence.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
|
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
