{"product_id":"hyperparameter-tuning-for-machine-and-deep-learning-with-r-a-practical-guide-9789811951695","title":"Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis open-access book offers practical examples and insights into hyperparameter tuning for machine learning and deep learning methods, aiming to improve results with reduced time, costs, effort, and resources. It includes case studies that can be executed on a regular computer and covers theory and application, with a focus on hyperparameter tuning of ML and DL algorithms. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 323 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 02 January 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis comprehensive open-access book offers a treasure trove of practical examples showcasing the application of hyperparameter tuning in the real world. It delves deep into the inner workings of machine learning (ML) and deep learning (DL) methods, providing invaluable insights for readers seeking to optimize their performance with minimal time, cost, effort, and resources.\u003cbr\u003e\u003cbr\u003eThe primary objective of this book is to empower readers with the skills to achieve exceptional results in a remarkably short timeframe, regardless of the resources at their disposal. The case studies presented herein can be executed on a standard desktop or notebook computer, eliminating the need for high-performance computing facilities.\u003cbr\u003e\u003cbr\u003eThe genesis of this book can be traced back to a study conducted by Bartz \u0026amp; Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building upon that foundation, the book is tailored to practitioners in industry, as well as researchers, teachers, and students in academia. The content revolves around hyperparameter tuning of ML and DL algorithms, organized into two main sections: theory (Part I) and application (Part II).\u003cbr\u003e\u003cbr\u003eWithin these sections, essential topics are covered, including:\u003cbr\u003e\u003cbr\u003eA comprehensive survey of important model parameters\u003cbr\u003eDetailed analysis of four parameter tuning studies and one extensive global parameter tuning study\u003cbr\u003eStatistical evaluation of the performance of ML and DL methods based on severity\u003cbr\u003eIntroduction of a novel consensus-ranking-based approach to aggregate and analyze results from multiple algorithms\u003cbr\u003e\u003cbr\u003eThe book presents comprehensive analyses of over 30 hyperparameters from six relevant ML and DL methods. Additionally, it includes comprehensive source code, allowing users to reproduce the results and further explore the intricacies of hyperparameter tuning. As such, the book serves as both a handbook and a textbook, offering valuable guidance and knowledge to practitioners and enthusiasts alike.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 682g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811951695\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2023\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Hardback","offer_id":44302283669754,"sku":"9789811951695","price":37.47,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_eaabd812-ed3b-496e-ad20-16dcfc736c28.jpg?v=1687924132","url":"https:\/\/shulphink.com\/products\/hyperparameter-tuning-for-machine-and-deep-learning-with-r-a-practical-guide-9789811951695","provider":"Shulph Ink","version":"1.0","type":"link"}