The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models

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Key benefits

Learn to diagnose the need for regularization in any machine learning model

Regularize different ML models using a variety of techniques and methods

Enhance the functionality of your models using state of the art computer vision and NLP techniques

Description

Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you’ll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You’ll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you’ll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you’ll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you’ll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you’ll be armed with different regularization techniques to apply to your ML and DL models.

What you will learn

Diagnose overfitting and the need for regularization

Regularize common linear models such as logistic regression

Understand regularizing tree-based models such as XGBoos

Uncover the secrets of structured data to regularize ML models

Explore general techniques to regularize deep learning models

Discover specific regularization techniques for NLP problems using transformers

Understand the regularization in computer vision models and CNN architectures

Apply cutting-edge computer vision regularization with generative models

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Yiqiao Yin Sep 07, 2023

In "The Regularization Cookbook," readers are provided an in-depth exploration into the world of regularization, offering a detailed roadmap from foundational concepts to advanced methodologies. The introduction establishes a firm grounding on the very essence of regularization, setting the stage for the chapters that follow.Upon establishing the basics, the book delves into practical applications, addressing the complexities of regularizing linear models such as logistic regression. It further extends its ambit to elucidate the intricacies involved in tree-based models, particularly the increasingly popular XGBoost. Such detailed treatment is both commendable and crucial for readers at various stages of their data science journey.A standout feature of this work is its comprehensive coverage of deep learning. With the burgeoning significance of Natural Language Processing (NLP) and computer vision in contemporary machine learning, the in-depth treatment of regularization methods tailored for Recurrent Neural Networks, transformers, and seminal models like BERT and GPT-3 is of paramount importance. These chapters promise to be both enlightening and essential for professionals aiming to harness the full power of these technologies.Furthermore, the segments on computer vision offer an expansive overview. Not only do they unravel the layers of Convolutional Neural Networks, but they also venture into the compelling domain of generative models, showcasing models like Dall-E.In summation, "The Regularization Cookbook" is a masterful compilation, adeptly spanning the breadth and depth of regularization in machine learning and deep learning. Whether a novice seeking foundational insights or an expert aiming for nuanced understanding, this book is poised to be an invaluable addition to one's scholarly repertoire.

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Ratan Aug 28, 2023

This books presents a fair and an organized way of regularization techniques for a lot of algorithms. This book is more on applied side rather than having mathematical rigorous proofs. It gives enough mathematical background to understand algorithms and how regularization can be applied on them. One good thing is - author connects how theoretical parameters are actually represented in scikit learn framework which is helpful if you aren’t a pro with Sklearn.I particularly liked the details about how regularization can be applied to language models which is interesting. If you are not looking for mathematical proofs and are on more applied ML side, this book is a good read overall. Author has presented topics in a very organized manner with good hands-on snippets.

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Om S Aug 13, 2023

Starting with the basics, the book unveils the importance of regularization and offers expert insights into diagnosing overfitting. From there, it delves into an array of techniques applicable to various machine learning models, including linear and tree-based models. The authors showcase how to apply these techniques effectively, ensuring a solid grasp of the concepts.A standout feature of the book is its dedication to real-world scenarios. It addresses challenges associated with high cardinality features and imbalanced datasets, offering tailored regularization methods. The deep learning sections are equally compelling, guiding readers through strategies for both general neural networks and NLP-specific applications like transformers, BERT, and GPT-3.What sets "The Regularization Cookbook" apart is its accessibility. While catering to experienced practitioners, it also accommodates those new to the field. The book provides Python codes and revisits fundamental concepts, ensuring a smooth learning curve for all readers.In a landscape where model optimization is paramount, "The Regularization Cookbook" emerges as an essential tool. It empowers readers to elevate their understanding of regularization, ultimately enhancing the performance, robustness, and reliability of their machine learning and deep learning models. Whether you're an enthusiast, data scientist, or machine learning professional, this book is your guide to becoming a regularization virtuoso.

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S.Kundu Sep 19, 2023

The Regularization Cookbook will been an awesome read if you want to explore different options to improve the functionality of your ML models.I am sharing my views regarding the same.The book will explain you what Regularization concept actually is along with giving you basic refresher of Machine Learning and Deep Learning so that it become easy for you to follow rest of the book.The book will teach you the different concepts of Regularization such as:Regularization with Linear models such as Ridge Regression, Lasso Regression and Logistic Regression.Regularization with Tree based models including Regularization of Decision Tree, Random Forest and XGBoost.Regularization with L2 Regularization, network architecture and dropout.Regularization with Recurrent Neural Networks with dropout and maximum length sequence. It will also discuss about training an RNN and GRU.Advanced Regularization in Natural Language Processing using word2vec, BERT and will discuss on data augmentation using word2vec and GPT-3.Regularization in Computer Vision along with synthetic image generation. It will discuss on Regularizing a CNN with vanilla NN methods and transfer learning. It will also cover topics on Spatial and pixel level augmentation.

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Gowtham Varma Bhupathiraju Oct 08, 2023

"The Regularization Cookbook" serves as a comprehensive guide to mastering regularization in the realm of machine learning. Seamlessly blending theoretical insights with hands-on code examples, it caters to both novices and seasoned professionals. The book's strength lies in its detailed overview of various regularization techniques, supplemented by the advantages and limitations of each. While it might not delve deep into certain intricate topics, it lays a robust groundwork for those eager to dive deeper. A must-read for anyone keen on enhancing their understanding of machine learning regularization.

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The Regularization Cookbook: Explore practical recipes to improve the functionality of your ML models

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