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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 #507 · 11/26/2017, 10:08 PM

http://devarea.com/machine-learning-with-python-introduction/#.Whs6iCehU8o #Machine_Learning With Python – Introduction #Numpy is package for multi dimension arrays – very effective implementation #Scipy – package for scientific programming , mathematics , signal processing and more #Pandas – package for data handling #Matplotlib – package for data visualization (graphs) #Seaborn – extend Matplotlib with statistical graphs #Scikits – many extensions to spicy for specific fields like x-ray, image processing , deep learning and many more