Grokking LLM

Vemula, Anand

ISBN 13: 9798332331886

Verlag: Amazon Digital Services LLC - Kdp, 2024

Beschreibung

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Bestandsnummer des Verkäufers L0-9798332331886

Bibliografische Details

• Titel: Grokking LLM
• Verlag: Amazon Digital Services LLC - Kdp
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-13: 9798332331886
• Einband: PAP
• Zustand: New

Über diesen Titel

Inhaltsangabe

Grokking LLM: From Fundamentals to Advanced Techniques in Large Language Models is a comprehensive guide that delves into the intricacies of Large Language Models (LLMs) and their transformative impact on natural language processing (NLP). This book is designed to take readers on a journey from the basic concepts of NLP to the advanced techniques used to train and deploy LLMs effectively.

The book begins with an introduction to LLMs, explaining their evolution, significance, and diverse applications in fields such as text generation, translation, and conversational AI. It provides a foundational understanding of the key components of LLMs, including tokens, embeddings, and the attention mechanism, alongside an overview of the Transformer architecture that underpins these models.

Readers will explore popular LLMs like GPT-3, GPT-4, BERT, and T5, learning about their unique characteristics, strengths, and use cases. A comparative analysis helps highlight the differences and performance metrics of these models, aiding in selecting the right model for specific applications.

Training large language models is covered in detail, from data collection and preprocessing to training objectives and fine-tuning techniques. The book also addresses the challenges of handling bias and ensuring fairness in LLMs, offering practical strategies for mitigation.

Implementing LLMs with Python and TensorFlow is a key focus, providing step-by-step guidance on setting up the environment, preparing data, and building and fine-tuning models. Readers will gain hands-on experience through practical projects such as building a text generator, creating a chatbot, and developing sentiment analysis and text summarization systems.

Advanced techniques like transfer learning, prompt engineering, zero-shot and few-shot learning, and distributed training are explored to equip readers with the skills needed for cutting-edge LLM applications. The book also covers performance optimization, model compression, quantization, and best practices for deploying LLMs in production environments.

With real-world case studies and insights into future trends and innovations, Grokking LLM: From Fundamentals to Advanced Techniques in Large Language Models is an essential resource for anyone looking to master the power and potential of LLMs in the rapidly evolving field of AI.