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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.
We based our evaluation of book condition on the following criteria:
* New: Just like it sounds. A brand-new, unused, unread copy in perfect condition.
* Like New: An apparently unread copy in perfect condition. Dust cover is intact; pages are clean and are not marred by notes or folds of any kind.
* Very Good: A copy that has been read, but remains in excellent condition. Pages are intact and are not marred by notes or highlighting, but may contain a neat previous owner name. The spine remains undamaged.
* Good: A copy that has been read, but remains in clean condition. All pages are intact, and the cover is intact. The spine may show signs of wear. Pages can include limited notes and highlighting, and the copy can include "From the library of" labels or previous owner inscriptions.
* Acceptable: A readable copy. All pages are intact, and the cover is intact (the dust cover may be missing). Pages can include considerable notes--in pen or highlighter--but the notes cannot obscure the text.
