PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N� de r�f. du vendeur L0-9781837634088

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

Vincent Vandenbussche

Edit� par Packt Publishing, 2023

ISBN 10 : 1837634084 ISBN 13 : 9781837634088

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

Vincent Vandenbussche

Edit� par Packt Publishing Limited, 2023

ISBN 10 : 1837634084 ISBN 13 : 9781837634088

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Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526. N� de r�f. du vendeur C9781837634088

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

Vincent Vandenbussche

Edit� par Packt Publishing, 2023

ISBN 10 : 1837634084 ISBN 13 : 9781837634088

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3Purchase of the print or Kindle book includes a free PDF Elektronisches BuchKey Features:Learn to diagnose the need for regularization in any machine learning modelRegularize different ML models using a variety of techniques and methodsEnhance the functionality of your models using state of the art computer vision and NLP techniquesBook 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 regularizationRegularize common linear models such as logistic regressionUnderstand regularizing tree-based models such as XGBoosUncover the secrets of structured data to regularize ML modelsExplore general techniques to regularize deep learning modelsDiscover specific regularization techniques for NLP problems using transformersUnderstand the regularization in computer vision models and CNN architecturesApply cutting-edge computer vision regularization with generative modelsWho this book is for:This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite. N� de r�f. du vendeur 9781837634088

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