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Source channel @githubtrending · Post #14737 · May 22

#other This book provides a systematic introduction to large language models (LLMs), covering topics like traditional language models, LLM architectures, prompt engineering, efficient parameter tuning, model editing, and retrieval-enhanced generation. It aims to be easy to read and rigorous, with monthly updates and a list of relevant papers. The book helps readers understand LLMs' principles and applications, making it beneficial for those interested in AI and NLP. It offers a structured learning path, which is useful for both beginners and advanced learners. https://github.com/ZJU-LLMs/Foundations-of-LLMs

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djangoproject

@djangoproject · Post #153 · 09/03/2016, 08:20 PM

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http://wla.berkeley.edu/~cs61a/fa11/lectures/streams.html In this chapter, we continue our discussion of real-world applications by developing new tools to process #sequential#data. In Chapter 2, we introduced a sequence interface, implemented in Python by built-in data types such as #tuple and #list. #Sequences supported two operations: querying their length and accessing an element by index. In Chapter 3, we developed a user-defined implementations of the sequence interface, the Rlist class for representing recursive lists. These sequence types proved effective for representing and accessing a wide variety of sequential #datasets.

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