foundations

An exploration of the philosophical nature of real numbers, questioning their reality and foundations in mathematics.

This book covers the mathematical foundations of data science, including high-dimensional analysis, SVD, PCA, regression, graphs, clustering, deep learning, and more.

This paper presents a comprehensive overview of metacognition in large language models, covering measurement methods, improvement techniques, and future directions.

A paper titled 'The Hitchhiker's Guide to Agentic AI: From Foundations to Systems' provides comprehensive textbook-like resources on the basics of LLMs and agentic AI.

Promotes a structured MIT deep learning course that covers foundations, generative models, agents, and sequence problems. The course aims to build practical understanding before advanced topics.

A document titled 'Deep Learning Foundations, Architectures & Engineering Practice' is shared, likely covering fundamental concepts, architectures, and practical engineering aspects of deep learning.

Recommend a book for systematically learning the basics of large language models: 《Foundations of Large Language Models》, written by Tong Xiao and Jingbo Zhu from Northeastern University NLP Lab and NiuTrans Research.

A 178-page survey study from the University of Huddersfield covering math and generative AI foundations, titled 'The Little Book of Generative AI Foundations'.

This book covers foundational concepts of large language models, including pre-training, generative models, prompting, and alignment. It serves as a reference for students and practitioners in NLP.