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Data Driven Science and Engineering by Steven Brunton, J Nathan Kutz is a nonfiction available to read on EtoBox.

What is Data Driven Science and Engineering about?

Preface Acknowledgments Optimization, Equations, Symbols, and Acronyms I Dimensionality Reduction and Transforms Singular Value Decomposition (SVD) Overview Matrix Approximation Mathematical Properties and Manipulations Pseudo-Inverse, Least-Squares, and Regression Principal Component Analysis (PCA) Eigenfaces Example Truncation and Alignment Randomized Singular Value Decomposition Tensor Decompositions and N-Way Data Arrays Fourier and Wavelet Transforms Fourier Series and Fourier Transforms Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) Transforming Partial Differential Equations Gabor Transform and the Spectrogram Laplace Transform Wavelets and Multi-Resolution Analysis Two-Dimensional Transforms and Image Processing Sparsity and Compressed Sensing Sparsity and Compression Compressed Sensing Compressed Sensing Examples The Geometry of Compression Sparse Regression Sparse Representation Robust Principal Component Analysis (RPCA) Sparse Sensor Placement II Machine Learning and Data Analysis Regression and Model Selection Classic Curve Fitting Nonlinear Regression and Gradient Descent Regression and Ax=b: Over- and Under-Determined Systems Optimization as the Cor

Who reads Data Driven Science and Engineering?

Author

Steven Brunton, J Nathan Kutz

Publisher

Cambridge University Press

Published

2021

Language

EN

Category

nonfiction

Subjects

Computer Science, Science, Engineering