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Large Language Models: A Practical Handbook for Designing, Building and Deploying Efficient LLMs from scratch

Large Language Models: A Practical Handbook for Designing, Building and Deploying Efficient LLMs from scratch

Large Language Models: A Practical Handbook for Designing, Building and Deploying Efficient LLMs from scratch Paperback - 2025

by CHAD SCOTT

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Details

  • Title Large Language Models: A Practical Handbook for Designing, Building and Deploying Efficient LLMs from scratch
  • Author CHAD SCOTT
  • Binding Paperback
  • Condition New
  • Pages 194
  • Volumes 1
  • Language ENG
  • Publisher Independently Published
  • Publication date 2025-01-08
  • Bookseller's Inventory # ria9798306347097_inp
  • ISBN 9798306347097
  • Weight 0.59 lbs (0.27 kg)
  • Dimensions 9 x 6 x 0.41 in (22.86 x 15.24 x 1.04 cm)
  • Category Computers - General Information
  • Quantity available 150

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From the publisher

Large Language Model: A Practical Handbook for Designing, Building, and Deploying Efficient LLMs from Scratch

Unlock the power of Large Language Models (LLMs) with this step-by-step guide that simplifies complex concepts into actionable insights. Whether you're a beginner or an AI enthusiast, this book equips you with the knowledge to design, train, and deploy state-of-the-art LLMs, empowering you to master real-world AI applications.

Why This Book?

Unlike other resources, this book emphasizes efficiency, scalability, and hands-on implementation, bridging the gap between theory and practice. With practical examples, clear instructions, and advanced topics like reinforcement learning and retrieval-augmented generation, it's your ultimate guide to building robust AI systems.

What You'll Learn:
  • Understand LLM Basics: Grasp core concepts, architectures, and applications.
  • Build Efficient LLMs: Learn to design scalable models optimized for real-world use.
  • Master Data Pipelines: Preprocess and manage large-scale text data effectively.
  • Train Models from Scratch: Develop and fine-tune your own LLMs.
  • Optimize Inference: Implement quantization and compression techniques for faster deployment.
  • Deploy at Scale: Use cloud platforms and distributed systems for production-ready models.
  • Explore Cutting-Edge Topics: Dive into RLHF, RAG pipelines, and more.
  • Operationalize with MLOps: Apply best practices for continuous monitoring and updates.
Unique Features:
  • Comprehensive coverage from foundational concepts to advanced techniques.
  • Hands-on examples and templates for practical implementation.
  • Focus on efficiency and scalability to reduce deployment costs.
  • Insights into ethical AI practices and emerging trends.

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