RAG: The link between pre-trained language models and real-time data
Retrieval Augmented Generation (RAG) is an advanced NLP technique combining retrieval and generation elements to enhance AI language models’ capabilities.
Ensemble models: Techniques, benefits, applications, algorithms and implementation
An ensemble model is a machine-learning approach where multiple models work together to make better predictions.
Knowledge graphs in machine learning: Significance, applications and development
Knowledge graphs in ML enable effective data governance by organizing, connecting data, providing context, and fostering intelligent insights for decision-making.
Supervised machine learning: Types, use cases, applications, operational mechanics, techniques and implementation
Supervised learning is a machine learning approach where a model is trained using labeled data to make predictions or classify new, unlabeled data.
A comprehensive exploration of various machine learning techniques
A machine learning algorithm is a set of mathematical rules and procedures that allows an AI system to perform specific tasks, such as predicting output or making decisions, by learning from data.
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.
