The 6 Foundational Courses To Learn Large Language Models

The text discusses six foundational courses for learning about Large Language Models (LLMs), which are revolutionizing the tech landscape with models like ChatGPT, Bard, LLaMA, and Claude. LLMs have potential implications beyond entertainment, such as replacing human jobs and introducing new professions dedicated to training and optimizing them. The text emphasizes the importance of understanding machine learning, deep learning, and natural language processing (NLP) fundamentals for comprehending LLMs. The six courses are divided into three phases: 1. Theory and Foundations: This phase includes 3Blue1Brown's series on linear algebra, calculus, and neural networks, which provide an intuitive understanding of the mathematical foundations required for machine learning. 2. NLP Basics: Sequence Models from Coursera by Andrew Ng focuses on sequence models used in processing text, while NLP – Natural Language Processing with Python by Jose Portilla covers text data preparation and transformation techniques. 3. Deep Dive into Modern Language Models: DS-GA 1008 - Deep Learning, NYU delves into the theoretical aspects of deep learning, CS324 - Large Language Models, Stanford University explores specific applications and LLM architectures, and COS 597G - Understanding Large Language Models, Princeton University investigates prompting, reasoning, in-context learning, and addressing issues like bias and toxicity. These courses provide a comprehensive understanding of the foundational theory, implementation, and recent developments in LLMs, catering to learners at various levels of expertise.