{"product_id":"linear-algebra-for-data-science","title":"Linear Algebra for Data Science","description":"This accessible yet rigorous textbook introduces the fundamentals of linear algebra in the context of real-world data science applications. Including the latest developments in the field, clear and detailed mathematical explanations. and extensive examples, it offers a comprehensive and approachable introduction to the subject, focusing on the foundations of the singular value decomposition and its many uses. Key topics include matrix subspaces, reduced-rank matrix approximation, angles between subspaces, averaging subspaces, spectral embedding algorithms including Laplacian eigenmaps and multidimensional scaling, the K-SVD dictionary learning algorithm, and the generalized singular value decomposition. The text takes a practical approach, featuring real-world application examples and more than 600 end-of-chapter exercises. Accompanying online resources include a solutions manual for instructors, data sets, and MATLAB and Python code for implementing algorithms in the text.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57502108287349,"sku":"9781009663717","price":65.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9781009663717.jpg?v=1786780924","url":"https:\/\/backstory.london\/products\/linear-algebra-for-data-science","provider":"Backstory","version":"1.0","type":"link"}