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Linear algebra and the foundations of deep learning, together at last!
| Publisher | Wellesley-Cambridge Press |
| Publication date | January 31, 2019 |
| Edition | First Edition |
| Language | English |
| Print length | 446 pages |
| ISBN-10 | 0692196382 |
| ISBN-13 | 978-0692196380 |
| Item Weight | 2.05 pounds (930 grams) |
| Reading age | 18 years and up |
| Dimensions | 7.72 x 0.98 x 9.53 inches (19.6 x 2.5 x 24.2 cm) |
Ideal for engineering students who require a strong foundation in linear algebra for applied technical courses.
Useful for data scientists looking to understand linear algebra concepts applied in machine learning and data analysis.
Beneficial for graduate researchers needing to apply linear algebra techniques in their thesis or research projects.
Not suitable for absolute beginners who have no prior knowledge of mathematics or linear algebra concepts.
Linear Algebra and Learning from Data
"Linear Algebra and Learning from Data", written by renowned mathematician Gilbert Strang, promises to provide a solid link between linear algebra and statistical modeling. However, the book seems to have divided opinions among customers. While some customers praised the content of the book and found it to be an excellent resource for brushing up on linear algebra, others were disappointed with the overall quality. One reviewer mentioned that the Kindle formatting made the book unintelligible, while another criticized the printing quality, claiming it appeared to be a poorly printed PDF. Additionally, a customer who had taken Strang's linear algebra course in the past and purchased several of his earlier books described this particular book as disjointed, disorganized, repetitious, and unreadable. They suspected that the positive ratings were due to respect for Strang rather than the book's actual quality. Despite the mixed reviews, there were a few positive aspects highlighted by customers. Some praised the book's coverage of Principal Component Analysis and its high-level overview of Fast Fourier Transformation. The book was also noted to have a collection of applications of linear algebra, making it a valuable resource in that regard. Overall, it seems that the customer experience with "Linear Algebra and Learning from Data" is somewhat polarized. While some found it to be a helpful refresher and appreciated Strang's pedagogy, others were disappointed with the formatting, printing quality, and overall organization of the book.
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