Bayesian networks in AI: Role in machine learning, example, types and applications
Bayesian networks are graphical models utilizing a Directed Acyclic Graph (DAG) to represent a group of random variables and their probabilistic relationships.
What is explainable AI? Use cases, benefits, models, techniques and principles
Explainable AI refers to a collection of processes and techniques that enable humans to comprehend and trust the outputs generated by machine learning algorithms.
How to use LLMs in synthesizing training data?
Harnessing the power of large language models (LLMs), a mighty tool capable of understanding, generating, and even refining human-like text we can generate synthesized training data that is flawless and train our models more efficiently.
Pattern recognition in ML: A comprehensive overview
Pattern recognition in machine learning enables systems to discover similarities and automatically extract meaningful information from a given dataset.
How to create a generative audio model?
Generative audio models can be used to create music, sound effects, and voices for various media projects such as films, video games, and virtual reality experiences.
Data security in AI systems: An overview
As AI continues to disrupt numerous business sectors, the importance of data security in AI systems becomes increasingly critical.
