{"product_id":"kernel-methods-for-machine-learning-with-math-and-python-100-exercises-for-building-logic-paperback","title":"Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic - Paperback","description":"\u003cp\u003eby \u003cb\u003eJoe Suzuki\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. \u003c\/p\u003e\u003cp\u003eThe book's main features are as follows: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThe content is written in an easy-to-follow and self-contained style.\u003c\/li\u003e\n\u003cli\u003eThe book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.\u003c\/li\u003e\n\u003cli\u003eThe mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.\u003c\/li\u003e\n\u003cli\u003eSource programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.\u003c\/li\u003e\n\u003cli\u003eOnce readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.\u003c\/li\u003e\n\u003cli\u003eThis book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. \u003c\/p\u003e\u003cp\u003eThe book's main features are as follows: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThe content is written in an easy-to-follow and self-contained style.\u003c\/li\u003e\n\u003cli\u003eThe book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.\u003c\/li\u003e\n\u003cli\u003eThe mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.\u003c\/li\u003e\n\u003cli\u003eSource programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.\u003c\/li\u003e\n\u003cli\u003eOnce readers have a basic understanding of the functional analysis topicscovered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.\u003c\/li\u003e\n\u003cli\u003eThis book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003eJoe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.\u003cbr\u003eHe is the author of a series of textbooks in machine learning published by Springer. \u003cbr\u003e- Statistical Learning with Math and R- Statistical Learning with Math and Python- Sparse Estimation with Math and R \u003cbr\u003e- Sparse Estimation with Math and Python- Kernel Methods for Machine Learning with Math and R - Kernel Methods for Machine Learning with Math and Python (This book)\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 208\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.47 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 15, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":41893217927303,"sku":"9789811904004","price":89.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/9c7ff1237ebaecf96b7a9e918b146d7c.webp?v=1734079734","url":"https:\/\/booksby.splitshops.com\/products\/kernel-methods-for-machine-learning-with-math-and-python-100-exercises-for-building-logic-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}