ASTERIX PUBLICATION
This blog explains why large language models sometimes generate incorrect but convincing responses. It explores how token prediction, training data limitations, and probabilistic generation lead to hallucinations, along with techniques used to reduce them.
This blog explains how recommendation systems personalize content on digital platforms. It covers key techniques such as collaborative filtering, matrix factorization, and deep learning models that analyze user behavior to predict and recommend relevant items.
This tutorial shows how to build a simple AI system that compares PDF resumes with a job description using embeddings and cosine similarity, then ranks candidates based on how closely their resumes match the role.