Libro adottato a Politecnico di Milano, a.a. 2026/2027 · 4 canali
«Griewank – Evaluating Derivatives» è adottato per Numerical Analysis For Machine Learning dal prof. Edie Miglio (Computer Science and Engineering, Electronics Engineering, High Performance Computing Engineering e altri corsi – Politecnico di Milano).
Come lo indica il docente: A. Griewank, A. Walther, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, SIAM, Anno edizione: 2008, ISBN: 978-0898716597
Argomenti del programma: 1. Numerical Linear Algebra Tools Singular value decomposition, principal components and best low rank matrix. Eigenvalues. Least square methods. QR factorization. Toeplitz matrices and shift invariant filters. Convolution. Randomized SVD 2. Automatic differentiation Wenger list, Direct Acyclic Graphs, Forward mode of AD Dual numbers Backward mode of AD 3.
Argomenti del programma: 1. Numerical Linear Algebra Tools Singular value decomposition, principal components and best low rank matrix. Eigenvalues. Least square methods. QR factorization. Toeplitz matrices and shift invariant filters. Convolution. Randomized SVD 2. Automatic differentiation Wenger list, Direct Acyclic Graphs, Forward mode of AD Dual numbers Backward mode of AD 3.
Argomenti del programma: 1. Numerical Linear Algebra Tools Singular value decomposition, principal components and best low rank matrix. Eigenvalues. Least square methods. QR factorization. Toeplitz matrices and shift invariant filters. Convolution. Randomized SVD 2. Automatic differentiation Wenger list, Direct Acyclic Graphs, Forward mode of AD Dual numbers Backward mode of AD 3.
Argomenti del programma: 1. Numerical Linear Algebra Tools Singular value decomposition, principal components and best low rank matrix. Eigenvalues. Least square methods. QR factorization. Toeplitz matrices and shift invariant filters. Convolution. Randomized SVD 2. Automatic differentiation Wenger list, Direct Acyclic Graphs, Forward mode of AD Dual numbers Backward mode of AD 3.
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