Mastering Large Language Models with PyTorch (Paperback)
Idioma: inglés
Editorial: Independently Published, 2024
- Tapa blanda
- Nuevo
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Vendedor de IberLibro desde 29 de junio de 2022
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EUR 23,45
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Paperback. In today's fast-paced world of artificial intelligence and natural language processing, large language models (LLMs) have emerged as a groundbreaking technology, transforming industries and enabling new applications. "Mastering Large Language Models with PyTorch" is your essential guide to understanding, building, and deploying these powerful models using the popular PyTorch framework. This comprehensive book provides you with the knowledge and tools to harness the full potential of LLMs through hands-on tutorials and practical code examples.The book begins with an accessible introduction to LLMs, explaining their significance and diverse applications. From chatbots and sentiment analysis to text generation and summarization, you'll discover how LLMs are revolutionizing the way we interact with technology. The guide also covers why PyTorch has become the preferred choice for researchers and developers, highlighting its flexibility, ease of use, and robust community support.Getting started with PyTorch is made easy with step-by-step instructions on installation, environment setup, and basic operations. You'll quickly learn to navigate the PyTorch ecosystem and start experimenting with simple neural networks. As you progress, the book delves deeper into the intricacies of LLMs, explaining key concepts and terminology, and comparing popular architectures such as GPT, BERT, and T5.Data preparation is a critical aspect of training LLMs, and this guide covers best practices for collecting, cleaning, and preprocessing text data. You'll also learn to create efficient datasets and data loaders, ensuring smooth and fast training processes. The book provides a detailed walkthrough of building LLMs from scratch, covering model architecture, attention mechanisms, and transformer blocks, all illustrated with clear, annotated code examples.Training and fine-tuning LLMs are covered extensively, with practical advice on optimizing performance and leveraging pretrained models for specific tasks. You'll explore advanced topics like mixed precision training, distributed training, and model compression techniques, equipping you with the skills to handle large-scale data and deploy models effectively.Real-world case studies and success stories demonstrate the impact of LLMs across various domains, while troubleshooting tips and best practices help you overcome common challenges. The book also connects you to valuable community resources and support, ensuring you stay updated with the latest advancements.Whether you're a beginner or an experienced practitioner, "Mastering Large Language Models with PyTorch" is your go-to resource for mastering the art of LLMs and applying them to solve real-world problems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
N° de ref. del artículo 9798327715714
- Título
- Mastering Large Language Models with PyTorch (Paperback)
- Autor
- Anand Vemula
- Editorial
- Independently Published
- Año de publicación
- 2024
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798327715714
In today's fast-paced world of artificial intelligence and natural language processing, large language models (LLMs) have emerged as a groundbreaking technology, transforming industries and enabling new applications. "Mastering Large Language Models with PyTorch" is your essential guide to understanding, building, and deploying these powerful models using the popular PyTorch framework. This comprehensive book provides you with the knowledge and tools to harness the full potential of LLMs through hands-on tutorials and practical code examples.
The book begins with an accessible introduction to LLMs, explaining their significance and diverse applications. From chatbots and sentiment analysis to text generation and summarization, you'll discover how LLMs are revolutionizing the way we interact with technology. The guide also covers why PyTorch has become the preferred choice for researchers and developers, highlighting its flexibility, ease of use, and robust community support.
Getting started with PyTorch is made easy with step-by-step instructions on installation, environment setup, and basic operations. You'll quickly learn to navigate the PyTorch ecosystem and start experimenting with simple neural networks. As you progress, the book delves deeper into the intricacies of LLMs, explaining key concepts and terminology, and comparing popular architectures such as GPT, BERT, and T5.
Data preparation is a critical aspect of training LLMs, and this guide covers best practices for collecting, cleaning, and preprocessing text data. You'll also learn to create efficient datasets and data loaders, ensuring smooth and fast training processes. The book provides a detailed walkthrough of building LLMs from scratch, covering model architecture, attention mechanisms, and transformer blocks, all illustrated with clear, annotated code examples.
Training and fine-tuning LLMs are covered extensively, with practical advice on optimizing performance and leveraging pretrained models for specific tasks. You'll explore advanced topics like mixed precision training, distributed training, and model compression techniques, equipping you with the skills to handle large-scale data and deploy models effectively.
Real-world case studies and success stories demonstrate the impact of LLMs across various domains, while troubleshooting tips and best practices help you overcome common challenges. The book also connects you to valuable community resources and support, ensuring you stay updated with the latest advancements.
Whether you're a beginner or an experienced practitioner, "Mastering Large Language Models with PyTorch" is your go-to resource for mastering the art of LLMs and applying them to solve real-world problems.
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