Linear algebra and learning from data
Gilbert Strang (Autore)
"This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first, especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data." -- Publisher's description
Libro a stampa, English, 2019
Wellesley-Cambridge Press, Wellesley, MA, 2019