Linear Algebra and Learning from Data
Linear Algebra and Learning from Data
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Linear Algebra and Learning from Data by Gilbert Strang
Linear algebra and the foundations of deep learning, together at last From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.Gilbert Strang is a well-known MIT mathematician who works in the fields of scientific research and other areas of applied mathematics.
| SKU | Unavailable |
| ISBN 13 | 9780692196380 |
| ISBN 10 | 0692196382 |
| Title | Linear Algebra and Learning from Data |
| Author | Gilbert Strang |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Wellesley-Cambridge Press,U.S. |
| Year published | 2019-01-31 |
| Number of pages | 446 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |