Product details
- ISBN 9781041090007
- Weight: 890g
- Dimensions: 178 x 254mm
- Publication Date: 17 Nov 2025
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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From fundamental concepts to advanced implementations, this book thoroughly explores the DeepSeek-V3 model, focusing on its Transformer-based architecture, technological innovations, and applications.
The book begins with a thorough examination of theoretical foundations, including self-attention, positional encoding, the Mixture of Experts mechanism, and distributed training strategies. It then explores DeepSeek-V3’s technical advancements, including sparse attention mechanisms, FP8 mixed-precision training, and hierarchical load balancing, which optimize memory and energy efficiency. Through case studies and API integration techniques, the model's high-performance capabilities in text generation, mathematical reasoning, and code completion are examined. The book highlights DeepSeek’s open platform and covers secure API authentication, concurrency strategies, and real-time data processing for scalable AI applications. Additionally, the book addresses industry applications, such as chat client development, utilizing DeepSeek’s context caching and callback functions for automation and predictive maintenance.
This book is aimed primarily at AI researchers and developers working on large-scale AI models. It is an invaluable resource for professionals seeking to understand the theoretical underpinnings and practical implementation of advanced AI systems, particularly those interested in efficient, scalable applications.
Jing Dai graduated from Tsinghua University with research expertise in data mining, natural language processing, and related fields. With over a decade of experience as a technical engineer at leading companies including IBM and VMware, she has developed strong technical capabilities and deep industry insight. In recent years, her work has focused on advanced technologies such as large-scale model training, NLP, and model optimization, with particular emphasis on Transformer architectures, attention mechanisms, and multi-task learning.
