Linear Algebra and Learning from Data
Product ID: 100748911
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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. Review: Good source for machine learning but not an introductory text - A good guide to linear algebra in machine learning but the material is not always well organised. The introductory section (a quarter of the book) covers a lot ground but don’t expect to be able to learn linear algebra from scratch as various aspects are omitted or only mentioned in passing. Review: The clear Algebra - Well done, very clear and explain in details .
| Best Sellers Rank | 488,424 in Books ( See Top 100 in Books ) 49 in Mathematical Modelling 563 in Higher Mathematical Education 865 in Popular Science Maths |
| Customer Reviews | 4.6 out of 5 stars 262 Reviews |
"**"
Good source for machine learning but not an introductory text
A good guide to linear algebra in machine learning but the material is not always well organised. The introductory section (a quarter of the book) covers a lot ground but don’t expect to be able to learn linear algebra from scratch as various aspects are omitted or only mentioned in passing.
G**M
The clear Algebra
Well done, very clear and explain in details .
S**E
Any book by Prof Strang, is a book worth owning.
Any book by Prof Strang, is a book worth owning. If you have an interest in an area of study that Prof Strang has written a textbook about, just buy his book and learn it cold. 'Linear Algebra and Learning from Data' is another ringer.
R**.
Clear on the Linear Algebra and focused on data science applications
Gilbert Strang, well known MIT professor and author, writes another book on Linear algebra. He put a lot of effort into making the material accessible and not assuming a background in linear algebra (matrices) so aimed at beginners. There is a bit of 'personal commentary' added to the text that is trying to make the public comfortable that wouldn't normally be in a text book but doesn't bother me much here. The added focus is on applications to Machine learning and other data extraction so it focuses on linear algebra that are useful for that purpose and how they are useful.
G**.
Uno dei migliori libri di G. Strang
Il miglior libro sulle applicazioni dell’Algebra Lineare alla data science
A**I
Abre muitas perspectivas, em termos de pesquisa, nos dois assuntos
Uso esse produto em minhas pesquisas acadêmicas.
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