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Fine-Tuning: Art of Adapting Machine Learning Models by Dave Richardson
English | May 8, 2025 | ISBN: N/A | ASIN: B0F84HLQ6K | 108 pages | EPUB | 1.37 Mb
Fine-Tuning is a comprehensive guide to one of the most powerful techniques in modern AI: adapting pretrained models to solve domain-specific problems with precision and efficiency. In an era where massive models dominate the field-from language to vision to audio-fine-tuning has become essential for achieving top-tier performance without incurring the massive costs of training from scratch.
This book demystifies the theory and practice of fine-tuning across a range of domains and model types. Whether you're a data scientist refining a language model for customer support, a machine learning engineer customizing a vision model for defect detection, or a researcher exploring low-resource fine-tuning techniques, this book equips you with the knowledge and tools to do it effectively.
Inside, you'll learn:The principles behind transfer learning and fine-tuning strategiesWhen and how to apply full, partial, or parameter-efficient fine-tuningBest practices for dataset preparation, training configuration, and evaluationTechniques tailored to NLP, computer vision, and audio applicationshow to deploy, scale, and monitor fine-tuned models in real-world systemsEmerging trends like LoRA, adapter tuning, and prompt-based methodsEthical considerations, bias mitigation, and responsible model adaptationPacked with practical examples, cutting-edge methods, and real-world case studies, Fine-Tuning is your essential reference for unlocking the full potential of pretrained models-and turning general intelligence into tailored, impactful solutions.
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