E-Books →Fine-Tuning Art of Adapting Machine Learning Models

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.