Linear Algebra For Data Science Machine Learning And Signal Processing Raj Rao Nadakuditi Cambridge University Press Αγγλικά Βιβλία Computing

Maximise Student Engagement And Understanding Of Matrix Methods In Data-Driven Applications With This Modern Teaching Package Students Are Introduced To Matrices Two Preliminary Chapters Before Progressing Advanced Topics Such As The Nuclear Norm Proximal Operators Convex Optimization Highlighted Include Low-Rank Approximation Completion Subspace Learning Logistic Regression For Binary Classification Robust Pca Dimensionality Reduction Procrustes Problems Extensively Classroom-Tested Book Includes Over 200 Multiple-Choice Questions Suitable In-Class Interactive Or Quizzes Well Homework Exercises (With Solutions Available Instructors) It Encourages Active Engaging \ Explore\ Answers At Back Each Chapter Julia Code Examples Demonstrate How Mathematics Is Actually Used Practice A Suite Computational Notebooks Offers Hands-On Experience Perfect Textbook Upper-Level Undergraduates First-Year Graduate Who Have Taken Prior Course Linear Algebra Basics Σελίδες: 450 Διαστάσεις: 17 6X17 6Cm