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Generative AI Handbook: A Roadmap for Learning Resources
Our focus in this section will be on quickly overviewing classical topics in statistical prediction and reinforcement learning, which we’ll make direct reference to in later sections, as well as highlighting some topics that I think are very useful as conceptual models for understanding LLMs, yet which are often omitted from deep learning crash courses – notably time-series analysis, regret minimization, and Markov models. Statistical Prediction and Supervised Learning Before getting to deep learning and large language models, it’ll be useful to have a solid grasp on some foundational concepts in probability theory and machine learning.
Like a lot of the topics discussed in this document, you can go quite deep down many different RL-related threads if you’d like; as it relates to language modeling and alignment, it’ll be most important to be comfortable with the basic problem setup for Markov decision processes, notion of policies and trajectories, and high-level understanding of standard iterative + gradient-based optimization methods for RL. This blog post from Simeon Carstens gives a nice coverage of Markov chain Monte Carlo methods, which are powerful and widely-used techniques for sampling from implicitly-represented distributions, and are helpful for thinking about probabilistic topics ranging from stochastic gradient descent to diffusion. With the rapid increase in parameter counts for leading LLMs and difficulties (both in cost and availability) in acquiring GPUs to run models on, there’s been a growing interest in quantizing LLM weights to use fewer bits each, which can often yield comparable output quality with a 50-75% (or more) reduction in required memory.
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