Exploring the fundamentals, methodologies, and applications of self-supervised learning, a technique revolutionizing AI by leveraging unlabeled data for representation learning.
A hands-on exploration comparing classical statistical methods (ARIMA, decomposition) with modern machine learning algorithms for forecasting air passenger traffic.
Utilizing Retrieval-Augmented Generation (RAG), vector databases, and a Language Model (LLM) to deliver accurate answers to user queries extracted directly from PDF files.