Gilbert Strang's Linear Algebra and Learning from Data
Linear algebra and the foundations of deep learning, together at last! From Prof. 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 students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Why you should read Linear Algebra and Learning from Data
by Gilbert Strang
This book has been written by Gilbert Strang, who has written books like Linear Algebra and Learning from Data,Introduction to Linear Algebra,Calculus,Linear Algebra and Its Applications,Wissenschaftliches Rechnen (Springer-Lehrbuch Masterclass). The books are written in Computer Science,Algebra & Trigonometry,Calculus,Science & Mathematics category. This book is read by people who are interested in reading books in category : Computer Science. So, if you want to explore books similar to This book, you must read and buy this book.
How long would it take for you to read Linear Algebra and Learning from Data
Depending on your reading style, this is how much time you would take to complete reading this book.
Time To Finish The Book
So if you are a Reader belonging in the Good category, and you read it daily for 1 hour, it will take you 29 days.
Note: A slow reader usually reads 100 words per minute, an average reader 200 words per minute, an average reader 300 words per minute and an excellent leader reads about 600-1000 words per minute, however the comprehension may vary.
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