May 1, 2024
Updated May 12, 2025
20 minute read
Hyperparameter tuning is a critical process in machine learning that involves selecting the optimal set of external configuration variables, known as hyperparameters, for a learning algorithm. These settings are not learned from the data itself but are set prior to the training process. The goal of hyperparameter tuning, also referred to as hyperparameter optimization, is to find the combination of these settings that yields the best model performance on a given dataset, minimizing a predefined loss function or maximizing a specific evaluation metric. This experimental practice is fundamental to coaxing the best performance out of machine learning models.
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Find a path to becoming a Hyperparameter Tuning. Learn more at:
OpenCourser.com/topic/3u9tn5/hyperparameter
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Hyperparameter Tuning.
Focuses specifically on hyperparameter tuning using Python. It covers various tuning methods, from basic to advanced, and provides practical examples. It's a good resource for practitioners looking for hands-on guidance and is published recently, making it relevant to contemporary practices.
Dives into hyperparameter optimization techniques for both machine learning and deep learning models. It covers different algorithms and frameworks, including Bayesian optimization. It's a step-by-step guide that helps solidify understanding and is valuable for those seeking to make their models more efficient.
Comprehensive guide to machine learning with Python, covering various algorithms and techniques. It includes sections on model evaluation and hyperparameter tuning, providing practical examples using popular Python libraries. It's a valuable resource for gaining a broad understanding and practical skills.
Must-read for anyone serious about statistical learning and provides a deep dive into the theory behind various algorithms, including discussions on hyperparameter tuning. It emphasizes understanding the underlying principles for effective model tuning and is highly regarded in academia and industry.
Covers AutoML with a focus on cloud platforms, including hyperparameter optimization as a key aspect. It's relevant for professionals working with cloud-based ML services and provides insights into automated tuning at scale.
Takes a code-first approach to deep learning, focusing on practical applications using fastai and PyTorch. It covers techniques relevant to hyperparameter tuning in the context of deep neural networks and is well-suited for practitioners and those who learn by doing.
A classic in the field, this book offers a comprehensive statistical approach to machine learning. It includes discussions on hyperparameter tuning strategies within various models. While not solely focused on tuning, it provides essential background knowledge and a solid theoretical foundation. It valuable reference for researchers and advanced students.
Provides a broad introduction to optimization techniques relevant to machine learning, including algorithms useful for hyperparameter tuning. It focuses on practical algorithms and their application, making it a good resource for understanding the optimization aspect of HPT.
Offers a practical approach to machine learning with Python, including a section on hyperparameter tuning with Keras Tuner. It's geared towards practitioners and provides hands-on examples for implementing tuning strategies.
This open access book provides a practical guide to hyperparameter tuning using the R programming language. It offers hands-on examples and insights into the working mechanisms of ML and DL methods. While focused on R, the concepts are transferable, making it valuable for understanding tuning principles.
Explores the intersection of optimization and machine learning, with chapters potentially relevant to hyperparameter tuning as an optimization problem. It presents modern advances and applications, suitable for researchers and those looking for a deeper understanding of the relationship between these fields.
Foundational text on numerical optimization, covering many techniques used in machine learning, including those relevant to hyperparameter tuning. It is mathematically focused and suitable for graduate students and researchers seeking a deep theoretical understanding.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/3u9tn5/hyperparameter
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