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Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
LLM Optimization.
Focuses on the use of transformers in NLP, including LLM optimization. It provides a good overview of the state-of-the-art techniques for optimizing LLMs.
Provides a comprehensive overview of large language models (LLMs), covering their history, architecture, training, and applications. It is written by two leading researchers in the field and is accessible to readers with a basic understanding of machine learning.
Provides a comprehensive overview of LLM optimization, covering techniques such as gradient descent, Adam, and RMSProp. It also discusses the challenges of optimizing LLMs, such as overfitting and catastrophic forgetting.
Provides a comprehensive overview of the foundations and applications of LLMs. It covers a wide range of topics, including LLM optimization.
Provides a practical guide to training and deploying deep learning models, including LLMs. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Covers a wide range of topics in NLP, including LLM optimization. It provides a good foundation for understanding the challenges and techniques involved in optimizing LLMs.
Covers a wide range of topics in NLP, including LLM optimization. It provides a good foundation for understanding the challenges and techniques involved in optimizing LLMs.
Provides a comprehensive overview of deep learning, including a chapter on LLM optimization. It classic textbook in the field and is suitable for readers with a strong foundation in mathematics and computer science.
Provides a detailed overview of optimization algorithms for machine learning, including a chapter on LLM optimization. It classic textbook in the field and is suitable for readers with a strong foundation in mathematics and computer science.
Provides a practical guide to training and deploying large-scale machine learning models, including LLMs. It covers topics such as data preprocessing, feature engineering, and model evaluation.
Provides a practical guide to training and deploying large-scale machine learning models, including LLMs, using the R programming language. It covers topics such as data preprocessing, feature engineering, and model evaluation.
Provides a general overview of machine learning, including a chapter on LLM optimization. It is written in a clear and accessible style and is suitable for readers with a basic understanding of mathematics and computer science.
Provides a comprehensive overview of convex optimization, which fundamental technique used in LLM optimization. It classic textbook in the field and is suitable for readers with a strong foundation in mathematics.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/fry3fo/llm
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