Building Generative AI: A Deep Dive Into LLM Architecture And Training - Couverture souple

Lucas Jr, Oliver

Couverture souple

ISBN 13 : 9798301436642

Editeur : Independently published, 2024

Synopsis

Dive deep into the world of Large Language Models (LLMs) and learn to build your own generative AI applications.

"Building Generative AI: A Deep Dive into LLM Architecture and Training" is a comprehensive guide to understanding, building, and deploying LLMs, the driving force behind the generative AI revolution.

This book takes you on a journey from the foundations of Natural Language Processing to the cutting edge of LLM research and development. You'll explore:

• Core concepts: Natural language processing, tokenization, embeddings, language modeling, and the Transformer architecture.1
• Building LLMs: Data acquisition and preparation, training techniques, optimization algorithms, and fine-tuning for specific tasks.
• Prompt engineering: Crafting effective prompts to guide LLM outputs and achieve desired results.2
• Evaluation and analysis: Metrics for assessing LLM performance, benchmark datasets, and human evaluation techniques.
• Multimodal LLMs: Integrating text with images and other data to expand LLM capabilities.3
• Ethical considerations: Addressing bias, misinformation, and responsible AI development.
• Deployment and scaling: Cloud platforms, APIs, model optimization, and real-world deployment strategies.
• The future of LLMs: Emerging trends, research directions, and the evolving landscape of AI-human collaboration.

Whether you're an AI enthusiast, a developer, or a researcher, this book will equip you with the knowledge and skills to navigate the exciting world of LLMs and build your own generative AI applications.

Unlock the power of LLMs and contribute to the future of AI.

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2024
• Langue: anglais
• ISBN 13: 9798301436642
• Reliure: Broché
• Nombre de pages: 86
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible