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"Model tuning" as a book category covers techniques, theory, and practical guidance for improving the performance, robustness, and efficiency of predictive models. Books in this genre explain hyperparameter tuning, regularization, optimization algorithms, learning-rate schedules, feature engineering, model selection, calibration, cross-validation, and evaluation metrics, and they survey search strategies like grid/random search, Bayesian optimization, and AutoML. They bridge theory (bias–variance tradeoff, convergence, generalization) with hands-on workflows and tools for fine‑tuning neural networks and classical ML models, including transfer learning, domain adaptation, and deployment considerations such as model compression and monitoring. Audience ranges from beginners wanting step‑by‑step tutorials to advanced practitioners and researchers seeking principled methods, case studies, and reproducible pipelines for squeezing the best performance out of real-world systems.

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