Linear Algebra and Learning from Data

Linear Algebra and Learning from Data

Author: Gilbert Strang

Publisher: Wellesley-Cambridge Press

ISBN: 0692196382

Category: Computers

Page: 446

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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.
Linear Algebra and Learning from Data
Language: en
Pages: 446
Authors: Gilbert Strang
Categories: Computers
Type: BOOK - Published: 2019-01-31 - Publisher: Wellesley-Cambridge Press

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
Lecture Notes for Linear Algebra
Language: en
Pages: 186
Authors: Gilbert Strang
Categories: Mathematics
Type: BOOK - Published: - Publisher:

Lecture Notes for Linear Algebra provides instructors with a detailed lecture-by-lecture outline for a basic linear algebra course. The ideas and examples presented in this e-book are based on Strang’s video lectures for Mathematics 18.06 and 18.065, available on MIT’s OpenCourseWare (ocw.mit.edu) and YouTube (youtube.com/mitocw). Readers will quickly gain a
Generatives Deep Learning
Language: de
Pages: 310
Authors: David Foster
Categories: Mathematics
Type: BOOK - Published: 2020 - Publisher:

Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige
Analysis and Linear Algebra: The Singular Value Decomposition and Applications
Language: en
Pages: 217
Authors: James Bisgard
Categories: Education
Type: BOOK - Published: 2020-10-19 - Publisher: American Mathematical Soc.

This book provides an elementary analytically inclined journey to a fundamental result of linear algebra: the Singular Value Decomposition (SVD). SVD is a workhorse in many applications of linear algebra to data science. Four important applications relevant to data science are considered throughout the book: determining the subspace that “best”
Linear Algebra and Optimization for Machine Learning
Language: en
Pages: 495
Authors: Charu C. Aggarwal
Categories: Computers
Type: BOOK - Published: 2020-05-13 - Publisher: Springer Nature

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use