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LLM Optimization

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Optimize LLMs for Specific Business Needs

Optimize LLMs for Specific Business Needs

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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.

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Natural Language Processing with Transformers,...

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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.

Natural Language Processing with Transformers,...

Natural Language Processing with Transformers,...

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Speech and Language Processing

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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.

Speech and Language Processing

Speech and Language Processing

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Cross-Lingual Word Embeddings

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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.

Cross-Lingual Word Embeddings

Cross-Lingual Word Embeddings

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Foundations of Statistical Natural Language...

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Provides a comprehensive overview of the foundations and applications of LLMs. It covers a wide range of topics, including LLM optimization.

Foundations of Statistical Natural Language...

Foundations of Statistical Natural Language...

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Hands-On Machine Learning with Scikit-Learn and...

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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.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn and...

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Python Machine Learning

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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.

Machine Learning with PyTorch and Scikit-Learn:...

Machine Learning with PyTorch and Scikit-Learn:...

Python Machine Learning: Machine Learning and Deep...

Python Machine Learning - Second Edition: Machine...

(Italiano) Machine Learning con Python - Nuova edizione:...

(Deutsch) Machine Learning mit Python und Keras, TensorFlow 2...

(Español) Machine Learning con PyTorch y Scikit-Learn:...

(Español) Python Machine Learning

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Machine Learning Algorithms

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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.

Mastering Machine Learning Algorithms - Second...

Mastering Machine Learning Algorithms: Expert...

Machine Learning Algorithms: Popular algorithms for...

Machine Learning Algorithms: A reference guide to...

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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.

Deep Learning (Adaptive Computation and Machine...

Deep Learning (Adaptive Computation and Machine...

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Numerical Optimization

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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.

Numerical Optimization (Springer Series in...

Numerical Optimization (Springer Series in...

(中文) Abroad mathematical classics series: Numerical...

Numerical Optimization (Springer Series in...

Numerical Optimization (Springer Series in...

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Machine Learning in Action

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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.

Machine Learning in Action

(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)

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The Elements of Statistical Learning

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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.

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

The Elements of Statistical Learning: Data Mining,...

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Grokking Deep Learning

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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.

Grokking Deep Learning

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Convex Optimization

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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.

Convex Optimization

Convex Optimization

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