Linear Algebra and Learning from Data
Original price was: $95,99.$19,99Current price is: $19,99.
- 100% Satisfaction Guaranteed!
- Immediate Digital Delivery
- Download Risk-Free
✔ Digital file type(s): １𝐏𝐃𝐅
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.
27 reviews for Linear Algebra and Learning from Data
You must be logged in to post a review.
Related products
-
Graph Theory and Additive Combinatorics: Exploring Structure and Randomness
Original price was: $69,99.$19,99Current price is: $19,99. -
Vector: A Surprising Story of Space, Time, and Mathematical Transformation
Original price was: $47,99.$19,99Current price is: $19,99. -
Causal Inference in Statistics – A Primer
Original price was: $50,95.$19,99Current price is: $19,99.
J. T. G. –
Prof Strang has been writing intoductory linear algebra books since the mid nineteen seventies. All of them good. In this book he sharply departs from his own and ever other book in introductory presentation and presents the outer product at the same level of detail as the inner product. This clarifies many applications, by allowing discussion of rank one corrections in applications. I really like this. For me it was late undergraduate or early graduate school that the outer and inner product became peer in the type of applications that I was interested in. While this book has an eye towards “machine learning,” it is very clear that Prof Strang sees all applications as data science. This book is his distillation of that. I wish I’d had this at the beginning of my education. This was a wonderful reintroduction to a view I use everyday.
Antonio M. –
Es un libro excelente, novedoso e innovador que me esta ayudando a mis clases en al Universidad
Sandip.B.Chajjed –
Love this book, intense mathematical but very well written, worth to have one.
Matthew –
This book relates two essential topics linear algebra and deep learning. Prof Strang sees statistics and optimization as two supplementary topics which bridge the main subjects. This book organizes central methods and ideas of data science and provides insight into how linear algebra gives expression to those ideas.
Mark Busenhart –
Gute Erklärung der wichtigen Aspekte und Zusammenhänge zwischen linearer Algebra, Optimierung, Statistik & Wahrscheinlichkeitsrechnung und Neuronalen Netzen.
Dr. Darrin Rasberry –
While not a full-on linear algebra book (despite the title), this does serve as a perfect undergraduate-level introduction to the Machine Learning galaxy and its many, many applications and increasingly popular methodology. Computer scientists, mathematicians and engineers – as well as math-savvy economists and businesspeople – could benefit from a class using this text or from self-learning if one is not prepared for a classic like Deep Learning.
Upsides include a thorough review of linear algebra and a very up-to-date list of data analytic topics that are at the edge of research and recently implemented techniques, including Google’s new ranking system, which it continues the recent trend of texts in covering as a side section late in the book. This is a Gil Strang book, which means problems, problems, problems galore (no answers are given within the text). Chapter VI on optimization methodology is the star of this show, and is a really good stepping stone to the higher-level texts on these topics.
Downsides include breadth-not-depth coverage and the need for a little bit of organization. There is far more in here for a single class, but it seems like it was written with the purpose to be all or almost all used for a single semester’s worth of coursework. This may work for MIT, but not for a second- or third-tier undergraduate department (at least not without significant trouble). Fortunately, an instructor can simply cut some topics, but the students should at least read up on these for the purposes of interest; they are certainly NOT comprehensive introductions – hence, the “breadth-not-depth” critique above; see e.g. the Compressed Sensing section for a prime example of … well, sparsity of information. It could also stand for some significant clean-up and organization.
Glibert Strang is well-known in math cirles as an extraordinary innovator and teacher of linear algebra. I found this book from him simply delightful, and, though my needs already exceeded it in general, was surprised on how much I learned from it still.
Suniyya –
It is written in a way that is conducive for self paced learning. Dr. Strang’s style of writing is engaging and very clear. The book fills an important gap and unifies ideas from different fields. Enjoying it a lot.
Perry –
Gilbert Strang is an amazing teacher. This is a great book.
Àlvar Martín Llopis –
Molt bon llibre sobre statistical learning!
gibonious –
Prof. Strang is just a masterful teacher, a teacher’s teacher. I have followed him for years via both his online lectures and his books, and neither this book nor the companion online course disappoint. It is all the more admirable that even late in his career he has stayed current in his field (he is quite the guru of applied linear algebra) and seems to always be striving for innovative ways to teach in both his lectures and his books. For those interested in learning both some of the basics of linear algebra and some of the more advanced topics that are pertinent to ML and data analysis, I highly recommend this book.
retired & reading –
Use it while watching Gilbert Strang on free MIT course.
Yongdam, Kim –
This textbook supports the MIT OCW lecture in detail with mathematical description.
Giulio G. –
Il miglior libro sulle applicazioni dell’Algebra Lineare alla data science
Luiz Velho –
intuition and insight!
John –
A lot of useful stuff that frees me from doing matrix calculation with brute force.
Interdependency –
An excellent survey on all essential topics. Dedicated and sharp
Philip W. –
This book presents linear algebra in a way that is different from most linear algebra approaches. Although most courses note that a matrix can be decomposed into r (rank of the matrix) outer products of two vectors (matrices of rank 1), this course exploits it to really expose many of the techniques of deep learning and fundamentally explain the factorization of matrices via LU, QR and SVD and circulant matrices. I rediscovered the factorization of X^N + Y^N quite easily from such techniques. It really helps in understanding many algorithms of “Numerical Recipes in C”. The only deficit is that it references so many other sources rather than expostulating the technique itself. Although in its defense, if it did, the book would be too heavy to carry.
“coachmarkwhite” –
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.
Zhao Liu –
Really good explanation of machine learning from a linear algebra perspective. 10/10
Avinash Sooriyarachchi –
This book along with some understanding of calculus and optimization would prime anyone to understand the current state of the art of Machine Learning. However I strongly recommend that the reader refresh their knowledge of basic linear algebra by going through Dr. Strang’s free videos of linear algebra from MIT OCW on youtube or by reading his excellent book on basic linear algebra
Michael Tracy –
Excellent coverage of Principal Component Analysis. Good high-level overview of Fast Fourier Transformation. It is not a complete chapter on Fourier analysis, but enough to understand how it works.
Has a collection of applications of linear algebra. Not a good first text book — use Strang’s Linear Algebra for that, but an excellent book for applications.
Evan –
I wanted to brush up on linear algebra just because it’s been so long since I’ve really gone through all the details. This is a great refresher course for me. And a lot of the conceptual pedagogy that Strang is so well known for really shines here. Maybe it’s because I am already familiar with the material, but Strang makes it feel like meeting an old friend for dinner.
MICHAEL T ANDERSON –
I’m preparing a statistics course with a large linear algebra component. I needed a good solid reference that links linear algebra to statistical modeling. So why not go with the guru?
Ana Isabel Bezerra Cavalcanti –
Uso esse produto em minhas pesquisas acadêmicas.
Steve –
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.
Nicholas Schlabach –
This has an incredible amount of information about applications of Linear Algebra all over Computer Science and beyond.
It is reasonably well explained, however Mike X Cohen’s Linear Algebra book “Theory, Intuition, Code” has much better explanations (although covering far fewer topics).
I would recommend using both books together.
Richard Frantz Jr. –
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.