Linear Algebra For Data Science Machine Learning And Signal Processing
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Author: Jeffrey A. Fessler
language: en
Publisher: Cambridge University Press
Release Date: 2024-04-30
Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.
Machine Learning for Signal Processing
Author: Max A. Little
language: en
Publisher: Oxford University Press, USA
Release Date: 2019
Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Linear Algebra and Learning from Data
Author: Gilbert Strang
language: en
Publisher: Wellesley-Cambridge Press
Release Date: 2019-01-31
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.