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Название: Hands-On Large Language Models: Language Understanding and Generation (Final Release) Автор: Jay Alammar, Maarten Grootendorst Издательство: O’Reilly Media, Inc. Год: 2024 Страниц: 428 Язык: английский Формат: True/Retail PDF, True EPUB Размер: 18.4 MB, 20.4 MB
AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in Deep Learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today.
You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings.
Large language models (LLMs) have had a profound and far-reaching impact on the world. By enabling machines to better understand and generate human-like language, LLMs have opened new possibilities in the field of AI and impacted entire industries. This book provides a comprehensive and highly visual introduction to the world of LLMs, covering both the conceptual foundations and practical applications. From word representations that preceded deep learning to the cutting-edge (at the time of this writing) Transformer architecture, we will explore the history and evolution of LLMs. We delve into the inner workings of LLMs, exploring their architectures, training methods, and fine-tuning techniques. We also examine various applications of LLMs in text classification, clustering, topic modeling, chatbots, search engines, and more. With its unique blend of intuition-building, applications, and illustrative style, we hope that this book provides the ideal foundation for those looking to explore the exciting world of LLMs. Whether you are a beginner or an expert, we invite you to join us on this journey to start building with LLMs.
This book also shows you how to: • Build advanced LLM pipelines to cluster text documents and explore the topics they belong to • Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers • Learn various use cases where these models can provide value • Understand the architecture of underlying Transformer models like BERT and GPT • Get a deeper understanding of how LLMs are trained • Understanding how different methodsAI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in Deep Learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. Large language models (LLMs) have had a profound and far-reaching impact on the world. By enabling machines to better understand and generate human-like language, LLMs have opened new possibilities in the field of AI and impacted entire industries. This book provides a comprehensive and highly visual introduction to the world of LLMs, covering both the conceptual foundations and practical applications. This book assumes that you have some experience programming in Python and are familiar with the fundamentals of Machine Learning. The focus will be on building a strong intuition rather than deriving mathematical equations. As such, illustrations combined with hands-on examples will drive the examples and learning through this book. This book assumes no prior knowledge of popular Deep Learning frameworks such as PyTorch or TensorFlow nor any prior knowledge of generative modeling. of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)
Prerequisites: This book assumes that you have some experience programming in Python and are familiar with the fundamentals of Machine Learning. The focus will be on building a strong intuition rather than deriving mathematical equations. As such, illustrations combined with hands-on examples will drive the examples and learning through this book. This book assumes no prior knowledge of popular Deep Learning frameworks such as PyTorch or TensorFlow nor any prior knowledge of generative modeling.
If you are not familiar with Python, a great place to start is Learn Python, where you will find many tutorials on the basics of the language. To further ease the learning process, we made all the code available on Google Colab, a platform where you can run all of the code without the need to install anything locally.
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Observability for Large Language Models Название: Observability for Large Language Models: Understanding and Improving Your Use of LLMs Автор: Phillip Carter Издательство: O’Reilly Media,...
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Hands-On Large Language Models: Language Understanding and Generation (Final Release) 
