May 1, 2024
Updated May 11, 2025
17 minute read
Regularization, at its core, is a technique used in machine learning and statistics to prevent a common pitfall known as overfitting. Overfitting occurs when a model learns the training data too well, including its noise and random fluctuations, to the point where it performs poorly on new, unseen data. Regularization helps to create models that generalize better to new data by discouraging overly complex models. This often involves adding a penalty to the model's learning process, effectively reigning in its complexity.
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Reading list
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Regularization.
Provides a comprehensive theoretical foundation for statistical learning methods, including various regularization techniques like Lasso and Ridge Regression. It valuable reference for graduate students and researchers, offering in-depth explanations and mathematical details. It is commonly used as a textbook in advanced machine learning and statistics courses.
Provides a comprehensive overview of regularization techniques in machine learning, covering both theoretical foundations and practical applications. It is highly recommended for readers who want to gain a deep understanding of regularization and its role in preventing overfitting.
The latest edition of Géron's popular book, updated to cover recent developments and best practices in machine learning and deep learning with updated libraries. It continues to provide practical guidance on implementing regularization techniques with concrete code examples. Highly relevant for staying current with applied regularization.
This is the Python edition of the popular 'An Introduction to Statistical Learning.' It covers the same core concepts, including linear model selection and regularization, but with labs and examples implemented in Python. This makes it highly relevant for users of Python-based machine learning frameworks and suitable for undergraduate and graduate students, as well as professionals.
Provides a rigorous mathematical treatment of regularization techniques. It covers the theoretical foundations of regularization and its applications in learning theory. However, it requires a strong background in mathematics and is more suitable for advanced readers.
这本中文书重点介绍了机器学习中的正则化技术。它涵盖了各种正则化方法及其在机器学习模型中的应用，适合对正则化有深入了解的中文读者。作者李航是中国科学院院士，在机器学习领域有很高的声誉。
A more accessible version of 'The Elements of Statistical Learning,' this book introduces fundamental concepts of statistical learning with a focus on applications in R. It covers linear model selection and regularization in detail, making it suitable for upper-level undergraduate students and those new to the field. Each chapter includes labs to solidify understanding through practical application.
Considered a foundational text in deep learning, this book covers regularization techniques specifically in the context of neural networks. It provides mathematical and conceptual background for understanding deep learning models and their training, including regularization methods to combat overfitting. It key reference for graduate students and researchers in AI and deep learning.
Authored by key researchers in the field, this book delves specifically into sparse statistical learning, with a major focus on the Lasso and related regularization methods. It provides a deep theoretical and applied understanding of sparsity-inducing regularization. This specialized text for researchers and graduate students focusing on this area.
Provides a practical guide to machine learning, including a chapter dedicated to regularization techniques. It offers clear explanations and hands-on examples, making it suitable for readers who want to apply regularization in their own machine learning projects.
This classic textbook covers a wide range of machine learning topics, including regularization. It provides a thorough theoretical treatment of regularization techniques and their applications in various machine learning models.
Offers a comprehensive introduction to pattern recognition and machine learning from a probabilistic perspective. It introduces central ideas like regularization and the bias-variance tradeoff, essential for a solid understanding of the topic. While not solely focused on deep learning, it provides a strong foundation in the principles behind many regularization methods used in the field. It is suitable for advanced undergraduates and graduate students.
Focuses on the practical aspects of building predictive models, including the application of regularization techniques. It provides intuition for the strengths and weaknesses of various models and methods without heavy mathematical emphasis, making it valuable for practitioners. It covers regularization as an alternative to OLS regression for wide data or data exhibiting multicollinearity.
This comprehensive text provides a broad and deep introduction to machine learning from a probabilistic standpoint. While extensive, it covers regularization within this framework, offering a different perspective on why regularization is effective. It's a valuable reference for graduate students and researchers with a strong mathematical background.
This textbook provides a conceptual overview of machine learning, including a chapter on regularization techniques. It offers a clear and intuitive explanation of regularization and its role in preventing overfitting.
This practical guide to machine learning covers regularization techniques among other topics. It provides hands-on examples and code snippets, making it suitable for readers who want to learn regularization in a hands-on manner.
This practical book guides readers through building deep learning models using Keras, a high-level neural networks API in Python. It covers various regularization techniques commonly used in deep learning, such as dropout, L1 and L2 regularization, from a practical, code-oriented perspective. It's ideal for practitioners and students who want to implement regularization.
Offers a practical approach to machine learning and deep learning using Python libraries. It covers the implementation of various algorithms and includes explanations and examples of applying regularization techniques in practice. It good resource for those who want to learn by doing and see how regularization is coded.
Offers a detailed exploration of pattern recognition, with a strong emphasis on neural networks and statistical methods. It discusses regularization within the context of training models to improve generalization. Suitable for advanced undergraduate and graduate students, it provides a solid theoretical basis.
This textbook provides a comprehensive overview of neural networks and deep learning, including dedicated sections on regularization techniques used in training deep models. It covers both the theoretical foundations and practical aspects, suitable for graduate students and researchers. It can serve as a valuable reference for understanding regularization in the context of complex neural architectures.
An earlier work by Bishop focusing specifically on neural networks, this book provides a solid statistical foundation for understanding neural network models and their training. It covers regularization techniques as methods for improving learning and generalization in neural networks. It is suitable for graduate or advanced undergraduate students.
Provides a rigorous theoretical treatment of fundamental machine learning concepts. It covers regularization from a theoretical perspective, discussing its role in generalization bounds and empirical risk minimization. It is best suited for graduate students and researchers with a strong mathematical background seeking a deep theoretical understanding.
Ce livre francophone couvre les méthodes statistiques appliquées à l'économie et à la gestion, y compris une section sur les techniques de régularisation. Il fournit des explications claires et des exemples pratiques, ce qui le rend adapté aux lecteurs francophones qui souhaitent comprendre la régularisation.
Appears to be a more recent publication specifically focusing on regularization techniques. Based on the title, it likely provides a practical guide to various regularization methods and their applications. It could be a valuable resource for practitioners looking for a focused and up-to-date reference on regularization.
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